Gaining Perspective on What We've Lost: The Reliability of Encoded Anecdotes in Historical Ecology
Bibliographic record
Abstract
Historical data are essential in fisheries management and conservation, especially for species that suffered significant population declines prior to ecological data collection. Within the field of historical marine ecology, studies have relied on anecdotal evidence, such as written accounts by explorers and interviews of different generations of resource users, to demonstrate the former abundance of certain species and the extent of their ranges. Yet, do we all agree on how these anecdotes are interpreted? This study examines the way that different people interpret anecdotes extracted from historical narratives. We outsource a survey to 50 randomly selected people using Amazon Mechanical Turk (www.mturk.com) and ask them to 'code' historical anecdotes based on their perceived abundance of species. We perform intercoder reliability tests to show that people's perceptions of historical anecdotes are generally consistent. The results speak to the reliability of using people's perceptions to acquire quantitative data, and provide novel insights into the use of anecdotal evidence to inform historical ecology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".