Collaboration in an Information Commons: key elements for successful support of e-literacy
Bibliographic record
Abstract
Information Commons service models generally include some element(s) of collaboration, whether it is for the delivery of technical support, e-literacy instruction, face to face and virtual services, integrated learning support or other innovative service delivery programs designed to support and enhance learning. Establishing a successful Information Commons facility requires strategic thinking and positioning as well as tactical or short-term planning. Strategic thinking and planning are essential to ensure that the facility and associated services are strongly aligned with the institutional mission, strategy and values. It facilitates the development of collaborative ventures as it presents a campus-wide rather than a unit-centric view. Tactical planning, on the other hand, will develop the detailed operational plans and procedures required for a smooth running service.This article will look at different Information Commons models, outline the strategic and operational processes required when establishing a successful collaborative information commons environment and present case studies of two Information Commons with different service models and collaborative support for e-literacy.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".