AnnuAl ConferenCe ProCeedings CAnAdiAn AssoCiAtion for the study of Adult eduCAtion/ l’AssoCiAtion CAnAdiAnne Pour les é’tude de l’éduCAtion des Adults
Bibliographic record
Abstract
4) that it occurs over the lifespan. These characteristics are reflective of learning connected to conferences, as they pro-vide opportunities for people to engage in lifelong learning in a variety of contexts and participation is usually volun-tary. At numerous stages in life, individuals may attend dif-ferent kinds of conferences to network with various groups and engage in learning around assorted topics that may re-late to their personal life or professional interests. While conferences are widely used in a variety of different fields, and numerous papers provide a summary of talks given at particular conferences (Coate & Tooher, 2010; Landsberger, 2007), little research has been conducted to determine how learning occurs, how it can be supported, and how challenges around fostering conferences as learn-ing sites may be addressed. A couple of examples of papers
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.162 | 0.024 |
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".