"Behind the Binoculars: Interviews with Acclaimed Birdwatchers" by Mark Avery & Keith Betton. 2015. [book review]
Bibliographic record
Abstract
The authors looked for a diversity of people born between 1930 to 1980.Note there are only three women.My casual observation is that I have seen a lower percentage of women when birding in the UK.In North America it is balanced.While they tried for diversity the first thing I noticed was all these hot-shot birders started before the age of 10.The older people often said they were loners and many said they were "closet" naturalists as birding was not approved by their peers.While a couple were rudely rebuffed by older birders, a number were fortunate to find mentors who boosted both their skills and their scientific purpose.One sad point arose.If I was to offer a ten year old boy a drive to the woods today I would be in deep trouble.My friend Dennis Rupert and I took out three young boys, with enlightened parents, and all grew to be great teenage birders.This ability to teach, encourage and guide the young is now sadly lost.In a similar vein these folks wandered alone, unsupervised,
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".