Writeshops: A Tool for Packaging and Sharing Field-based Experiences (Case Studies)
Bibliographic record
Abstract
Writeshops were originally used by IIRR and some its early partners, mainly to<br> address the issue of poor engagement of field workers in the process of<br> documentation, learning and sharing of knowledge. Successful field projects whose<br> impacts remained localized were another issue to be addressed. Ways had to be found to increase their influences in the wider community. The challenges posed by the<br> limited uptake of exemplary practices, and the reality that useful knowledge often<br> remained in the mind of workers or in reports and unpublished documents, prompted<br> the discovery and testing of writeshop approaches . This compilation of experiences or Cases, is part of the twin pack that goes along with<br> the Guidelines. It is aimed at building evidence on use and efficacy of the<br> methodology as well as helping users in dealing with some common challenges.<br> Cases described cover a large part of the globe and have been produced by those<br> involved in designing, conducting and using writeshops.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".