Bibliographic record
Abstract
Abstract ‘Fishermen’ and the gender‐neutral ‘fishers’ are the most common terms used to describe people who fish in the English language. However, there is a considerable debate as to which term is most appropriate. In academic journals, usage of ‘fishers’ for people who fish began in the 1960s and increased over time, until in 2013 and 2014 ‘fishers’ first exceeded usage of ‘fishermen’, despite being labelled ‘archaic’ in the Oxford English Dictionary. In journal searches ‘fishermen’ unambiguously referred to people who fish, but ‘fishers’ also referred to R.A. Fisher's statistical tests (22%), the mammal, Pekania pennanti (5%), and other terms. Journal policies have played a role in the shift to ‘fishers’: e.g. Conservation Biology requires the use of ‘fishers’ while Fishery Bulletin requires ‘fishermen’. Partly as a result, in academia there are disciplinary and geographic variations, with greatest usage of ‘fishers’ in the field of conservation biology and in Australia. Surprisingly, word choice did not differ by the gender of the lead author. Many other languages also have gender‐neutral terms for people who fish (e.g. Austronesian and Turkic languages), yet the word is still assumed to refer to men. While the gender‐neutral term ‘fisher’ is more inclusive it is far from universally accepted, particularly by women and men in the North American fishing industry. The current shift towards the more inclusive term ‘fishers’ highlights the increasing disciplinary diversity within fisheries science, particularly in terms of gender.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".