Workshop Effectiveness on Content Knowledge of Behavioral Observation Techniques in an Applied Animal Behavior Context
Bibliographic record
Abstract
Comparative psychology has a long history of investigating topics that promote comparisons across disciplines, constructs, and species. One critical component of comparative analyses is to select the best data collection technique. Unfortunately, these observational skills are not always taught to individuals who need them the most, animal care professionals. To demonstrate the applicability of appropriate data collection techniques to this applied discipline, we conducted a multi-day workshop that provided attendees training and practice with several data collection techniques that could be used to evaluate animal behavior in both spontaneous and enrichment-provided settings. The program included (1) a presentation on different data collection techniques and the types of questions each technique can address, (2) two 20-minute sessions of observation practice at two different facilities, (3) a final summary presentation of the data collected, and (4) pre- and post-surveys conducted immediately before and at the end of the workshop. Out of 177 survey respondents, almost a third reported using behavioral data collection to manage animal behavior prior to the workshop. More than 90% of the respondents had heard of behavioral ethograms and 68% of the respondents had used one previously. Many of the respondents reported familiarity with different observation techniques. Eighty-two individuals completed the majority of the survey with 81% expressing satisfaction with the initial workshop presentation. Respondents completing both surveys showed significant improvement in their knowledge of behavioral data collection techniques. Ultimately, the workshop introduced and clarified behavioral observation techniques and their applications in a variety of contexts. Respondents indicated that they could and would utilize knowledge gained from the workshop at their own facilities.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".