Call for Proposals for 2020 Conference Now Open! The Action Research Network of the Americas
Bibliographic record
Abstract
After a very successful and inspiring #ARNA2019 conference in Montreal, Québec, Canada this past June, we are excited to begin planning for Peurto Vallarta, Mexico in 2020! This of course all starts with a call for proposals. We encourage you and your colleagues to submit an abstract that you think works well within the theme: Co-creando conocimiento, empoderando a la comunidad / Co-creating knowledge and empowering communities. Submission Guidelines can be found at www.arnawebsite.org/call-for-proposals and your opportunity to submit will be open until March 1, 2020. Once you have reviewed the guidelines, use the linked Google Form to submit your proposal. We look forward to your submission and seeing you at #ARNA2020 in Mexico!
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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.585 | 0.392 |
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".