Anecdotes in Primatology: Temporal Trends, Anthropocentrism, and Hierarchies of Knowledge
Bibliographic record
Abstract
ABSTRACT Formal narrative descriptions of primates have long been used by primatologists to describe novel events that are not captured by other data collection methods. However, there has been a shift away from narrative accounts toward more quantitative methods both within primatology and more broadly in the natural sciences. Our objective was to investigate the shifting use of anecdotal evidence in primatology. We systematically reviewed anecdotal accounts published in the four major primatology journals since the year 2000. We found 163 published anecdotal accounts out of 3,960 total articles published between 2000 and 2016. There was an overall decrease in the rates of anecdotes published during this time. Those published covered a wide range of topics and taxa but were skewed toward larger, diurnal primates—in particular, apes. We suggest that anecdotal evidence should continue to be published but that the publication of these data should better reflect the taxonomic diversity of primates. We also suggest potential venues for anecdote publication that may compensate for their loss from formal scientific journals. [narratives, qualitative data, anthropomorphic, primates, observation]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.199 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".