23 Things Revisited: Participant perceptions of a staff development program over a year later
Bibliographic record
Abstract
23 Things programs have been used around the world to help educate library workers about web 2.0 applications. This paper describes the results of two feedback surveys for a 23 Things program that was offered to library employees at the University of Saskatchewan: the first survey, conducted at the time of program completion, and a follow up survey, conducted a year and a half later to assess whether participants were applying what they learned in the program, and if so, how. Generally speaking, respondents indicated that the program was beneficial, and all but 3 of the respondents from the second survey had applied some of what they learned through the program, either personally or professionally. Despite being an independent learning activity, social aspects that arose organically as participants worked their way through the program seemed to be valued by program participants as much as the newly acquired knowledge from the lessons. The semi-facilitated format, in addition to the flexibility of completing the lessons at a convenient time and place, was key to the program’s success. Organizations that wish to transition long-time employees from the print-based working world to the digital world should consider making 23 Things style learning opportunities a regular part of their staff development activities.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".