From repositories supported by communities to communities supported by repositories
Bibliographic record
Abstract
The Carrick Institute for Teaching and Learning in Higher Education is developing the Carrick Exchange to provide a forum for sharing resources and expertise about learning and teaching. This paper reflects on relevant experience in North America with related initiatives, which highlight (a number of) issues to be considered and (a few) lessons which can be incorporated in the design and development of the Carrick Exchange. Most of the experience cited here comes from the MERLOT network, including both the MERLOT discipline community Editorial Boards and related communities such as the Cooperative Learning Object Exchange in Canada, the disciplinary Teaching Commons sites within the California State University, and the new MERLOT Innovation Projects such as ELIXR creating reusable resources for staff development. This paper also analyses the resulting reflections in the context of an independently-developed taxonomy for distributed collaborations in a parallel domain: large-scale scientific collaboratories. This analysis suggests that a full range of possibilities needs to be explored across dimensions such as aggregation versus co-creation and the range of valuable contributions of resources, information and knowledge. Another conclusion is that a number of user needs can be met without the full infrastructure of a distributed community of practice.
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 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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.018 | 0.055 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".