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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".