Catalysts of Open Education in Colorado: A Qualitative Study of Enabling Forces in OE Momentum
Bibliographic record
Abstract
What are/were the catalysts that enabled Open Education (OE) momentum in Colorado, and what can be gleaned from its origin stories? Using a mix of qualitative methods (e.g. interviews, narrative analysis, discourse analysis), this paper maps the forces, both actual and imagined, that enabled OE to flourish across the state. This paper locates patterns specific to Colorado and analyzes the interdependent and interpersonal aspects of the OE movement/philosophy there. It arrives at the conclusion that two themes in particular (state-level support and community characteristics) contribute to Colorado’s reputation as an OE leader. Rather than view these as distinct forces, the two themes entwine and synergistically enhance the other. This paper contributes to growing research in the area of second-order OE thriving and sustainability. It makes the case that, while identifying barriers to OE can assist with action-oriented research, identifying the enabling forces can also offer a more nuanced understanding in a particular place: less of the bad is one tactic, more of the good is another.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".