When Management Defines Leadership: High Demand x High Support in a Rural Community College
Bibliographic record
Abstract
Drawing upon a diversity of data from efforts across almost four decades – the last two in the same rural community college – devoted to developing, implementing and studying the impact of a High Demand x High Support (HDxHS) teaching pedagogy, this paper addresses four topics: First, we briefly address the values and perspectives informing the HDxHS effort. Second, we sketch the HDxHS pedagogy and describe case exemplars, situating these in the context of a rural community college (CC). Third, we describe different leadership responses to HDxHS in terms of the use of strategic ignorance strategies (SIS), suggesting that these likely function more as a barrier than a conduit for understanding the challenges facing rural communities and CCs. Fourth, acknowledging that CCs are currently at a crossroads facing an uncertain future as legitimate public post-secondary institutions, we outline elements of a re-scripted more democratic CC leadership model. Part overview and part summary, the conclusion addresses the strengths and weaknesses of the HDxHS approach and the various bodies of knowledge to which it might contribute.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".