Leading new, deeper forms of collaborative cultures: Questions and pathways
Bibliographic record
Abstract
The pandemic has made deeper problems more transparent and has stimulated many to realize that there may be an opportunity over the next period to pursue much needed innovations in learning. In this essay we describe the ways in which the pandemic has provided the conditions for new human development that joins two powerful forces: the pulsar model which elevates human potential with respect to student learning, and new, deeper forms of collaboration that have long eluded those interested in system change. In this article we show how 'spirit work' and collaboration can combine to develop schools systems that are essential for coping with the new post-pandemic conditions facing humanity. We also identify spinoff opportunities arising from the pandemic, and a corresponding pressure that could generate more widespread system improvement designed to improve learning for all, including advances in both equity and excellence.
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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 | 0.041 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.086 |
| Scholarly communication | 0.041 | 0.060 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".