Distance education policy : a study of the SREB faculty support policy construct at four virtual college and university consortia.
Bibliographic record
Abstract
The present study has a three pronged purpose: one, describe how the faculty support policy construct developed by the Southern Regional Education Board (SREB) exists at four Virtual Colleges and Universities Consortia (VCU). Two, describe how VCUs degree of centralization and emphasis on business practices influences the faculty support policy construct of their respective sampled institutions. Three, search for patterns in policy characteristics across the same four VCUs accounting for their degree of centralization and emphasis on business practices. The study is among the first in-depth qualitative studies to apply SREB's faculty support policy construct to representative VCUs of the Epper and Garn taxonomy, delve into specific details of the faculty support policy construct proposed by SREB, and search for policy patterns among representative VCUs selected for the study. The study provides much needed insight that is currently missing from the literature and that should assist university leaders, policy makers, and faculty in the administration of day-to-day activities at Virtual Colleges and Universities Consortia or academic collaborations. The study design is a multiple-case study. The design facilitated obtaining better insight, description, and discovery of how the faculty support policy construct exists today at the selected VCUs, how the construct influences the operation of each VCU, and if patterns exist in faculty support characteristics among the four institutions. The design encouraged a high level and comprehensive understanding of the phenomenon under study, distance learning policy, and the development of general theoretical statements. Data gathering techniques were semi-structured phone interviews and document analysis. Study findings revealed that the SREB faculty support policy construct exists at the four sample institutions with very distinct levels of intensity. Findings also revealed that sampled VCUs degree of centralization and business practice influence some faculty support policies implemented at the sampled higher education institutions. Lastly, findings reveal that patterns exist across higher education institutions in terms of faculty support policies. While some patterns diverge from the Epper and Garn taxonomy most patterns are just expected and consistent across higher education institutions.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".