Bibliographic record
Abstract
This paper is based on all text received since the conference was announced late in 2009. Prospective participants were informed that no individual papers were expected but that the conference would be structured around one document, baptized ‘Lead-paper’, that would go through several rounds of amendments, additions, criticisms and alterations in the months ahead, aiming at a discussion document that would already represent views of participants and would allow us to begin our conference with a common knowledge based on our exchange of data, views, and questions, making the conference the more fruitful. This proposition led to a 1st version of the Lead-paper (5 pages, 2000 words) December 30, 2009, going through a 2nd edition (31 January 2010, 12 pages, 5152 words), a 3rd edition (1 March, 50 pages, 17.850 words) and a 4th edition (70 pages plus 14 pages attachments, 29.777 words), e-mailed on March 26, 2010, to all participants and distributed in Deventer at the opening session on 8 April 2010 in print. This 4th edition ‘Lead-paper’ was used as the substantial agenda for the conference on 8-9 April 2010 in Deventer. This is the 5th edition which incorporates substantial thoughts, criticism, questions, and remarks contributed in writing, by the participants and from other sources.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.378 | 0.157 |
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".