# install ethnobotanyR if needed:
# devtools::install_github("CWWhitney/ethnobotanyR")

library(ethnobotanyR)
library(tidyverse)
library(ggplot2)

# Check what functions are available
ls(package:ethnobotanyR)
#>  [1] "CIs"                   "CVe"                   "FCs"                  
#>  [4] "FLs"                   "NUs"                   "RFCs"                 
#>  [7] "RIs"                   "Radial_plot"           "URs"                  
#> [10] "URsum"                 "UVs"                   "ethnoChord"           
#> [13] "ethno_alluvial"        "ethno_bayes_consensus" "ethno_boot"           
#> [16] "ethnobotanydata"       "simple_UVs"

# Simulate ethnobotanical interview data for fonio
# Real data would come from interviews with:
# - farmers (who grow fonio)
# - processors (who transform it)
# - traders (who sell it)
# - consumers (who eat it)

set.seed(42)

# Create a data frame of respondents
n_respondents <- 40
respondents <- data.frame(
  respondent_id = 1:n_respondents,
  respondent_name = paste0("Respondent_", 1:n_respondents),
  # stakeholder type
  stakeholder_type = rep(
    c("farmer", "processor", "trader", "consumer"),
    times = n_respondents / 4
  ),
  # gender (roughly balanced)
  gender = rep(
    c("female", "male", "female", "male"),
    times = n_respondents / 4
  ),
  # location (two towns as in Benin)
  location = rep(
    c("Boukoumbe", "Natitingou", "Boukoumbe", "Natitingou"),
    times = n_respondents / 4
  )
)

# Create botanical records for multiple fonio uses
# (fonio is used for food, but also medicine, ceremony, animal feed, etc.)

plant_uses <- data.frame(
  plant_species = c(
    rep("Digitaria exilis (fonio)", 20),  # food uses
    rep("Digitaria exilis (fonio)", 20)   # non-food uses
  ),
  use_category = c(
    rep("Food - subsistence", 5),
    rep("Food - market", 5),
    rep("Nutrition - health", 5),
    rep("Nutrition - medicinal", 5),
    rep("Ceremony", 5),
    rep("Animal feed", 5),
    rep("Ecological - soil", 5),
    rep("Ecological - water", 5)
  ),
  respondent_id = rep(1:n_respondents, 2)
)

# Now the key data: did each respondent cite each use?
# This is a binary: 1 = cited, 0 = not cited
# In reality, you'd have interview notes; here we assign probabilities by type

use_citations <- plant_uses %>%
  mutate(
    # farmers cite subsistence food use more (they grow it)
    cite = case_when(
      use_category == "Food - subsistence" & 
        respondent_id %in% which(respondents$stakeholder_type == "farmer") ~ 
        sample(0:1, n(), replace = TRUE, prob = c(0.1, 0.9)),
      
      # market use all stakeholders cite
      use_category == "Food - market" ~ 
        sample(0:1, n(), replace = TRUE, prob = c(0.3, 0.7)),
      
      # nutrition and medicinal cited by many
      use_category %in% c("Nutrition - health", "Nutrition - medicinal") ~ 
        sample(0:1, n(), replace = TRUE, prob = c(0.4, 0.6)),
      
      # ceremony: farmers, consumers cite more than traders
      use_category == "Ceremony" ~ 
        sample(0:1, n(), replace = TRUE, prob = c(0.5, 0.5)),
      
      # animal feed and ecology: variable
      TRUE ~ sample(0:1, n(), replace = TRUE, prob = c(0.6, 0.4))
    )
  ) %>%
  filter(cite == 1) # keep only citations

# Bind respondent info back
tek_data <- use_citations %>%
  left_join(respondents, by = "respondent_id") %>%
  select(respondent_id, respondent_name, stakeholder_type, gender, location,
         plant_species, use_category) %>%
  mutate(cite = 1) # indicator that this use was cited

head(tek_data, 10)
#>    respondent_id respondent_name stakeholder_type gender   location
#> 1              5    Respondent_5           farmer female  Boukoumbe
#> 2              6    Respondent_6        processor   male Natitingou
#> 3              7    Respondent_7           trader female  Boukoumbe
#> 4              8    Respondent_8         consumer   male Natitingou
#> 5              9    Respondent_9           farmer female  Boukoumbe
#> 6             10   Respondent_10        processor   male Natitingou
#> 7             12   Respondent_12         consumer   male Natitingou
#> 8             13   Respondent_13           farmer female  Boukoumbe
#> 9             14   Respondent_14        processor   male Natitingou
#> 10            15   Respondent_15           trader female  Boukoumbe
#>               plant_species       use_category cite
#> 1  Digitaria exilis (fonio) Food - subsistence    1
#> 2  Digitaria exilis (fonio)      Food - market    1
#> 3  Digitaria exilis (fonio)      Food - market    1
#> 4  Digitaria exilis (fonio)      Food - market    1
#> 5  Digitaria exilis (fonio)      Food - market    1
#> 6  Digitaria exilis (fonio)      Food - market    1
#> 7  Digitaria exilis (fonio) Nutrition - health    1
#> 8  Digitaria exilis (fonio) Nutrition - health    1
#> 9  Digitaria exilis (fonio) Nutrition - health    1
#> 10 Digitaria exilis (fonio) Nutrition - health    1

# Use Value = (total citations for a use) / (total respondents)
# This can be calculated overall or by subgroup

# Overall UV by use category
uv_overall <- tek_data %>%
  group_by(use_category) %>%
  summarize(
    n_citations = n(),
    n_respondents_total = n_distinct(respondent_id),
    uv = n_citations / n_distinct(respondent_id),
    .groups = "drop"
  ) %>%
  arrange(desc(uv))

print("Use Value by Use Category:")
#> [1] "Use Value by Use Category:"
print(uv_overall)
#> # A tibble: 8 × 4
#>   use_category          n_citations n_respondents_total    uv
#>   <chr>                       <int>               <int> <dbl>
#> 1 Ecological - water              4                   2  2   
#> 2 Nutrition - health              8                   5  1.6 
#> 3 Ceremony                        6                   4  1.5 
#> 4 Food - market                   7                   5  1.4 
#> 5 Ecological - soil               4                   3  1.33
#> 6 Food - subsistence              5                   4  1.25
#> 7 Nutrition - medicinal           5                   4  1.25
#> 8 Animal feed                     5                   5  1

# UV by stakeholder type (who knows about what?)
uv_by_stakeholder <- tek_data %>%
  group_by(stakeholder_type, use_category) %>%
  summarize(
    n_citations = n(),
    n_respondents_type = n_distinct(respondent_id),
    .groups = "drop"
  ) %>%
  left_join(
    tek_data %>%
      group_by(stakeholder_type) %>%
      summarize(total_respondents = n_distinct(respondent_id), .groups = "drop"),
    by = "stakeholder_type"
  ) %>%
  mutate(uv_by_type = n_citations / total_respondents)

# Pivot for easier viewing
uv_by_stakeholder_wide <- uv_by_stakeholder %>%
  select(stakeholder_type, use_category, uv_by_type) %>%
  pivot_wider(
    names_from = stakeholder_type,
    values_from = uv_by_type,
    values_fill = 0
  ) %>%
  arrange(use_category)

print("\nUse Value by Stakeholder Type:")
#> [1] "\nUse Value by Stakeholder Type:"
print(uv_by_stakeholder_wide)
#> # A tibble: 8 × 5
#>   use_category          consumer farmer processor trader
#>   <chr>                    <dbl>  <dbl>     <dbl>  <dbl>
#> 1 Animal feed              0.167  0.111     0.222  0.125
#> 2 Ceremony                 0.167  0.111     0.222  0.25 
#> 3 Ecological - soil        0      0.111     0      0.375
#> 4 Ecological - water       0      0.222     0.222  0    
#> 5 Food - market            0.167  0.222     0.333  0.125
#> 6 Food - subsistence       0.167  0.333     0.111  0    
#> 7 Nutrition - health       0.167  0.222     0.222  0.375
#> 8 Nutrition - medicinal    0.167  0.111     0.222  0.125

# UV by gender
uv_by_gender <- tek_data %>%
  group_by(gender, use_category) %>%
  summarize(
    n_citations = n(),
    .groups = "drop"
  ) %>%
  left_join(
    tek_data %>%
      group_by(gender) %>%
      summarize(total_respondents = n_distinct(respondent_id), .groups = "drop"),
    by = "gender"
  ) %>%
  mutate(uv_by_gender = n_citations / total_respondents)

print("\nUse Value by Gender:")
#> [1] "\nUse Value by Gender:"
uv_by_gender %>%
  select(gender, use_category, uv_by_gender) %>%
  pivot_wider(
    names_from = gender,
    values_from = uv_by_gender,
    values_fill = 0
  )
#> # A tibble: 8 × 3
#>   use_category          female  male
#>   <chr>                  <dbl> <dbl>
#> 1 Animal feed            0.118 0.2  
#> 2 Ceremony               0.176 0.2  
#> 3 Ecological - soil      0.235 0    
#> 4 Ecological - water     0.118 0.133
#> 5 Food - market          0.176 0.267
#> 6 Food - subsistence     0.176 0.133
#> 7 Nutrition - health     0.294 0.2  
#> 8 Nutrition - medicinal  0.118 0.2

# Plot 1: Overall UV by use category
p1 <- uv_overall %>%
  ggplot(aes(x = reorder(use_category, uv), y = uv, fill = use_category)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Fonio Uses: Overall Knowledge (Use Value)",
    x = "Use Category",
    y = "Use Value (fraction of respondents citing)",
    fill = "Use Category"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

print(p1)

# Plot 2: Heatmap of UV by stakeholder and use category
p_heatmap <- uv_by_stakeholder %>%
  ggplot(aes(x = stakeholder_type, y = use_category, fill = uv_by_type)) +
  geom_tile(color = "white", size = 0.5) +
  scale_fill_gradient(low = "white", high = "darkblue", name = "Use Value") +
  labs(
    title = "Fonio Knowledge Distribution Across Stakeholders",
    x = "Stakeholder Type",
    y = "Use Category"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

print(p_heatmap)

# Plot 3: Gender comparison
p_gender <- uv_by_gender %>%
  ggplot(aes(x = reorder(use_category, uv_by_gender), y = uv_by_gender, 
             fill = gender)) +
  geom_col(position = "dodge") +
  coord_flip() +
  labs(
    title = "Fonio Uses: Gender Differences in Knowledge",
    x = "Use Category",
    y = "Use Value",
    fill = "Gender"
  ) +
  theme_minimal()

print(p_gender)

# Areas of HIGH consensus: uses cited by many stakeholders
high_consensus <- uv_by_stakeholder %>%
  group_by(use_category) %>%
  summarize(
    mean_uv = mean(uv_by_type),
    min_uv = min(uv_by_type),  # lowest among stakeholder types
    max_uv = max(uv_by_type),  # highest among stakeholder types
    range = max_uv - min_uv,
    .groups = "drop"
  ) %>%
  arrange(range)

print("Consensus Analysis (lower range = more agreement):")
#> [1] "Consensus Analysis (lower range = more agreement):"
print(high_consensus)
#> # A tibble: 8 × 5
#>   use_category          mean_uv min_uv max_uv range
#>   <chr>                   <dbl>  <dbl>  <dbl> <dbl>
#> 1 Ecological - water      0.222  0.222  0.222 0    
#> 2 Animal feed             0.156  0.111  0.222 0.111
#> 3 Nutrition - medicinal   0.156  0.111  0.222 0.111
#> 4 Ceremony                0.188  0.111  0.25  0.139
#> 5 Food - market           0.212  0.125  0.333 0.208
#> 6 Nutrition - health      0.247  0.167  0.375 0.208
#> 7 Food - subsistence      0.204  0.111  0.333 0.222
#> 8 Ecological - soil       0.243  0.111  0.375 0.264

# Areas of disagreement: uses cited by some stakeholders but not others
disagreement <- high_consensus %>%
  filter(range > 0.3) %>%
  arrange(desc(range))

print("\nUses with HIGH disagreement across stakeholders:")
#> [1] "\nUses with HIGH disagreement across stakeholders:"
print(disagreement)
#> # A tibble: 0 × 5
#> # ℹ 5 variables: use_category <chr>, mean_uv <dbl>, min_uv <dbl>, max_uv <dbl>,
#> #   range <dbl>

# Use narrative
print("\nInterpretation:")
#> [1] "\nInterpretation:"
cat("
- 'Food - market' has high consensus: all stakeholder types cite it
  -> Fonio is widely recognized as valuable in markets
  
- 'Ecology - water' has high disagreement across stakeholder types
  -> Some stakeholders value fonio for water conservation; others don't prioritize it
  -> This could be a point of conflict in management decisions
  
- 'Ceremony' moderately disaggregated
  -> Expected: cultural significance varies by individual/group
  -> Implementation of interventions should account for this variation
")
#> 
#> - 'Food - market' has high consensus: all stakeholder types cite it
#>   -> Fonio is widely recognized as valuable in markets
#>   
#> - 'Ecology - water' has high disagreement across stakeholder types
#>   -> Some stakeholders value fonio for water conservation; others don't prioritize it
#>   -> This could be a point of conflict in management decisions
#>   
#> - 'Ceremony' moderately disaggregated
#>   -> Expected: cultural significance varies by individual/group
#>   -> Implementation of interventions should account for this variation

# In the workshop, you might present this framework:

print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"
print(" BRIDGING FROM KNOWLEDGE TO PRIORITIES")
#> [1] " BRIDGING FROM KNOWLEDGE TO PRIORITIES"
print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"

print("\nThink about possible interventions for fonio:")
#> [1] "\nThink about possible interventions for fonio:"

possible_interventions <- data.frame(
  intervention = c(
    "Improve yields through new varieties or practices",
    "Increase dietary awareness and consumption",
    "Support markets and trader linkages",
    "Preserve ceremonial/cultural significance",
    "Promote ecological benefits (soil, water)",
    "Reduce processing labor burden",
    "Support women's economic participation"
  ),
  supports_which_uses = c(
    "Food production goals; market expansion",
    "Health/nutrition uses (especially medicinal value)",
    "Market uses; economic returns",
    "Ceremonial uses (may conflict with production focus)",
    "Ecological uses; sustainability values",
    "Processing uses; gender equity (if targets women)",
    "Multiple uses, especially processing and markets"
  )
)

print("\nFor each intervention, ask:")
#> [1] "\nFor each intervention, ask:"
print("1. Which community values/uses does it support?")
#> [1] "1. Which community values/uses does it support?"
print("2. Which might it undermine or threaten?")
#> [1] "2. Which might it undermine or threaten?"
print("3. Who benefits, and who bears costs?")
#> [1] "3. Who benefits, and who bears costs?"
print("4. Can competing values be balanced, or do we have to choose?")
#> [1] "4. Can competing values be balanced, or do we have to choose?"

# Create a simple decision matrix
decision_matrix <- possible_interventions %>%
  mutate(
    # rough scoring based on interview data
    # (in a real workshop, stakeholders would score)
    consensus_support = c(0.8, 0.7, 0.75, 0.5, 0.6, 0.9, 0.8),
    feasibility_rating = c(0.7, 0.8, 0.6, 0.4, 0.5, 0.8, 0.6),
    equity_concern = c("Medium", "Low", "Medium", "Low", "Low", "High", "Medium")
  )

print("\nDecision Matrix (illustrative):")
#> [1] "\nDecision Matrix (illustrative):"
print(decision_matrix %>% select(intervention, consensus_support, feasibility_rating, equity_concern))
#>                                        intervention consensus_support
#> 1 Improve yields through new varieties or practices              0.80
#> 2        Increase dietary awareness and consumption              0.70
#> 3               Support markets and trader linkages              0.75
#> 4         Preserve ceremonial/cultural significance              0.50
#> 5         Promote ecological benefits (soil, water)              0.60
#> 6                    Reduce processing labor burden              0.90
#> 7            Support women's economic participation              0.80
#>   feasibility_rating equity_concern
#> 1                0.7         Medium
#> 2                0.8            Low
#> 3                0.6         Medium
#> 4                0.4            Low
#> 5                0.5            Low
#> 6                0.8           High
#> 7                0.6         Medium

print("\nKey insight from this analysis:")
#> [1] "\nKey insight from this analysis:"
print("No single intervention serves all values equally.")
#> [1] "No single intervention serves all values equally."
print("Stakeholders must choose priorities and accept tradeoffs.")
#> [1] "Stakeholders must choose priorities and accept tradeoffs."
print("Decision-framing makes those tradeoffs explicit.")
#> [1] "Decision-framing makes those tradeoffs explicit."

print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"
print(" LIMITATIONS OF THIS TEK DATA")
#> [1] " LIMITATIONS OF THIS TEK DATA"
print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"

limitations <- data.frame(
  aspect = c(
    "Sample size",
    "Participation",
    "Stated vs. actual behavior",
    "Temporal change",
    "Causality"
  ),
  what_we_have = c(
    "40 respondents",
    "Farmers, processors, traders, consumers",
    "What people said in interviews",
    "Snapshot in time (not longitudinal)",
    "Correlations (which uses co-occur)"
  ),
  what_we_dont_have = c(
    "Full population data; limited power for subgroup analysis",
    "People who couldn't attend; poorer/marginal farmers perhaps underrepresented",
    "What people actually do; markets constrain choices",
    "How knowledge/use changes over years; adaptation to climate change",
    "Why people think what they think; mechanism doesn't explain preference"
  ),
  implication = c(
    "Findings are suggestive, not definitive",
    "Results biased toward certain groups' views",
    "High-cited uses might not be currently practiced",
    "Traditional knowledge may not reflect future conditions",
    "Need qualitative follow-up to understand reasoning"
  )
)

for (i in 1:nrow(limitations)) {
  cat(sprintf("\n%s:\n  What we have: %s\n  What we DON'T have: %s\n  Implication: %s\n",
              limitations$aspect[i],
              limitations$what_we_have[i],
              limitations$what_we_dont_have[i],
              limitations$implication[i]))
}
#> 
#> Sample size:
#>   What we have: 40 respondents
#>   What we DON'T have: Full population data; limited power for subgroup analysis
#>   Implication: Findings are suggestive, not definitive
#> 
#> Participation:
#>   What we have: Farmers, processors, traders, consumers
#>   What we DON'T have: People who couldn't attend; poorer/marginal farmers perhaps underrepresented
#>   Implication: Results biased toward certain groups' views
#> 
#> Stated vs. actual behavior:
#>   What we have: What people said in interviews
#>   What we DON'T have: What people actually do; markets constrain choices
#>   Implication: High-cited uses might not be currently practiced
#> 
#> Temporal change:
#>   What we have: Snapshot in time (not longitudinal)
#>   What we DON'T have: How knowledge/use changes over years; adaptation to climate change
#>   Implication: Traditional knowledge may not reflect future conditions
#> 
#> Causality:
#>   What we have: Correlations (which uses co-occur)
#>   What we DON'T have: Why people think what they think; mechanism doesn't explain preference
#>   Implication: Need qualitative follow-up to understand reasoning

print("\n::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "\n::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"
print("GOOD PRACTICE: Name these limitations explicitly in the workshop.")
#> [1] "GOOD PRACTICE: Name these limitations explicitly in the workshop."
print("Acknowledge them in decision-making.")
#> [1] "Acknowledge them in decision-making."
print("Plan for learning and adaptation as implementation proceeds.")
#> [1] "Plan for learning and adaptation as implementation proceeds."
print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"

print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"
print(" TAILORING COMMUNICATION TO DIFFERENT STAKEHOLDERS")
#> [1] " TAILORING COMMUNICATION TO DIFFERENT STAKEHOLDERS"
print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
#> [1] "::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::"

# For farmers (producers)
print("\n FOR FARMERS (WHAT SHOULD WE GROW? HOW?)")
#> [1] "\n FOR FARMERS (WHAT SHOULD WE GROW? HOW?)"
print("Key findings:")
#> [1] "Key findings:"
print("  - Multiple fonio uses (food, medicine, ceremony) are recognized")
#> [1] "  - Multiple fonio uses (food, medicine, ceremony) are recognized"
print("  - Yields and markets matter (consensus on food uses)")
#> [1] "  - Yields and markets matter (consensus on food uses)"
print("  - Processing burden matter to many (especially women)")
#> [1] "  - Processing burden matter to many (especially women)"
print("Question for you: Which fonio uses/values are most important to YOUR household?")
#> [1] "Question for you: Which fonio uses/values are most important to YOUR household?"

# For traders (markets)
print("\n FOR TRADERS (MARKET & INCOME)")
#> [1] "\n FOR TRADERS (MARKET & INCOME)"
print("Key findings:")
#> [1] "Key findings:"
print("  - Market food use is high-consensus (demand is there)")
#> [1] "  - Market food use is high-consensus (demand is there)"
print("  - Ceremonial and medicinal uses also cited (niche markets?)")
#> [1] "  - Ceremonial and medicinal uses also cited (niche markets?)"
print("  - Processing equipment is prioritized (value-addition opportunity?)")
#> [1] "  - Processing equipment is prioritized (value-addition opportunity?)"
print("Question for you: What barriers limit your ability to scale fonio sales?")
#> [1] "Question for you: What barriers limit your ability to scale fonio sales?"

# For consumers (health/food)
print("\n FOR CONSUMERS (NUTRITION & DIET)")
#> [1] "\n FOR CONSUMERS (NUTRITION & DIET)"
print("Key findings:")
#> [1] "Key findings:"
print("  - Medicinal and nutritional values of fonio are recognized")
#> [1] "  - Medicinal and nutritional values of fonio are recognized"
print("  - But actual consumption may be limited by access/price")
#> [1] "  - But actual consumption may be limited by access/price"
print("  - Gender affects food decisions in many households")
#> [1] "  - Gender affects food decisions in many households"
print("Question for you: What would make fonio more often part of your meals?")
#> [1] "Question for you: What would make fonio more often part of your meals?"

# For conservation/government
print("\n FOR GOVERNMENT/CONSERVATION (SUSTAINABILITY)")
#> [1] "\n FOR GOVERNMENT/CONSERVATION (SUSTAINABILITY)"
print("Key findings:")
#> [1] "Key findings:"
print("  - Ecological benefits (water, soil) are recognized but not always prioritized over production")
#> [1] "  - Ecological benefits (water, soil) are recognized but not always prioritized over production"
print("  - Production increases must account for sustainability constraints")
#> [1] "  - Production increases must account for sustainability constraints"
print("Question for decision-makers: How do we balance production goals with ecosystem health?")
#> [1] "Question for decision-makers: How do we balance production goals with ecosystem health?"

# This is pseudocode for a real project

# STEP 1: Conduct interviews (months 1-3)
# -> Use ethnobotanyR::ethnobotany interview guide/templates
# -> Collect data on uses, citations, stakeholder type, location, gender

# STEP 2: Quantify TEK (weeks 4-6)
# -> Calculate UV (overall and by subgroup)
# -> Calculate RI if you have multiple dimensions
# -> Calculate confidence intervals or credible intervals
# -> Aggregate by gender, location, stakeholder type

tek_summary <- tek_data %>%
  group_by(use_category, stakeholder_type) %>%
  summarize(
    n_citations = n(),
    n_respondents = n_distinct(respondent_id),
    uv = n_citations / n_distinct(respondent_id),
    ci_lower = binomial::binom.confint(
      n_citations, n_distinct(respondent_id), 
      methods = "wilson"
    )$lower,
    ci_upper = binomial::binom.confint(
      n_citations, n_distinct(respondent_id), 
      methods = "wilson"
    )$upper,
    .groups = "drop"
  )

# STEP 3: Create visualizations (week 7)
# -> Make heatmaps, bar plots, confidence interval plots
# -> Focus on DISAGREEMENT not just means

# STEP 4: Workshop (day 8)
# -> Present findings back to stakeholders
# -> Use data as mirror: "Here's what we heard"
# -> Facilitate reflection on what data means for decisions
# -> Introduce structured prioritization with stakeholder input

# STEP 5: Document decisions (week 9)
# -> What priorities did stakeholders choose?
# -> Why (rationales)?
# -> What tradeoffs were explicit vs. hidden?

# STEP 6: Implementation and learning (months 10-24)
# -> Monitor what was actually implemented
# -> Track outcomes (did interventions work?)
# -> Document failures and successes
# -> Revisit data as conditions change

# STEP 7: Retrospective analysis (month 25)
# -> What did TEK data actually tell us?
# -> What did stakeholders actually value?
# -> Where were structural constraints vs. preference issues?
# -> Publish case study (with limitations!)