Focus on the forest, not the trees: a checklist for planning chapter meetings
Bibliographic record
Abstract
This poster was presented at the 2016 Joint Meeting of the Medical Library Association and the Canadian Health Sciences Library Association in Toronto CA in May 2016. Objective: After successfully planning a meeting of multiple MLA chapters, the authors share the lessons they learned from their experience. Methods: After reviewing successes and failures of their multi-chapter meeting, the authors used Survey Monkey to gather the opinions and advice of other multiple chapter meeting planners. The authors then identified key activities and time-sensitive tasks necessary to planning such a meeting. Results: From this amalgam of information, they created a checklist designed to help future planners, whether for individual or multiple chapter meetings. This checklist also includes a recommended timeline for when essential milestones should be reached. Conclusions: Holding a multi-chapter meeting, while a daunting task, can be beneficial to chapters and attendees. A vetted planning checklist, along with strong communication, skills with shared decision-making, and effective record-keeping are key components for success.
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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 | 0.101 | 0.229 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".