“I Love Being Able to Have my Colleagues Around the World at my Fingertips:”
Bibliographic record
Abstract
Teacher-librarians are often “lone wolves” in schools. This chapter explores how Canadian teacher-librarians are participating in life-long learning in the 21st century using Web 2.0 technologies. It also explores how one online distance education program implemented changes to help prepare teacher-librarians to participate in local and global personal learning networks. Findings from a Canadian survey on this topic found that teacher-librarians often seek out other teacher-librarians for advice and support, as well as relying on regular interaction (both face-to-face and online) with their colleagues. Other informal professional learning occurs through listservs, online networks, Elluminate sessions, webinars, TED talks, podcasts, Nings, blogs, and Twitter. New and emerging technologies are helping teacher-librarians connect to one another locally and, more importantly, globally. It is this combination of both local and global personal learning networks that helps teacher-librarians move from being lone wolves to members of the pack.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.036 |
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".