Seventh Annual Association of Adaptation Studies Conference, University of York, York, 27–28th September, 2012
Bibliographic record
Abstract
The critical current that determined the flow of the Seventh Annual Conference of the Association of Adaptation Studies, entitled ‘Visible and Invisible Authorships’, was Roland Barthes’s famously deceased Author-God. However, the deity was capriciously evident days before as a truly Biblical amount of rain caused York to flood. If there was ever an appropriate location for a rain-soaked conference, though, it was the University of York with its legendary ‘duck density’. International delegates battled the elements to arrive in the leafy, light environs of the Berrick Saul Building: the heart of humanities research at York. Expanding beyond the conference’s physical location, the Association of Adaptation Studies (AAS) promoted an event, for the first time, with a single hashtag that unified delegates participating via Twitter. Appointed ‘Tweeters’ Anna Blackwell (De Montfort University) and Catherine Oakley (University of York) led a steady stream of conversation that, by the end of the conference, became a cascade of pithy live commentary from Laurence Raw (Baskent University), Alex Beaumont (University of York), and Nicola McCartney (Birkbeck, University of London). Despite the relatively small pool of users, Twitter proved a successful instrument for discussion and disseminated the conference’s highlights worldwide. It is hoped the 2013 AAS conference at Linnæus University will similarly explore social media’s possibilities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.288 | 0.066 |
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".