POLICY NETWORKS AND COMMUNITIES IN THREE WESTERN CANADA UNIVERSITIES: NEO-INSTITUTIONAL RESPONSES TO A PAN - INSTITUTIONAL ISSUE
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this study was to describe, analyze and provide and understanding of the \nprocess of policy making on an ill-defined pan institutional issue (teaching and learning \ntechnology) within three western Canadian universities in two western Canadian \nprovinces. \nThe conceptual framework informing this study was Coleman & Skogstad and Atkinson \n& Colman's policy network and community model. More abstract organizational \ntheoretical frameworks provide the basis for a post hoc interpretation of the policy \nfindings, where post critical social organization models provide a basis for further \ndevelopment of the framework capacity. \nThe study was conducted in and around three large universities or cases from a potential \nsample of over 100,000 actors. The description, analysis and interpretation of the policy \nmaking process in these cases was conducted at the actor (micro), institution or sector \n(meso) and macro (policy environment) levels. The focus was on the changing \nuniversity policy leadership found within a disaggregated state, where a broad policy \ndevelopment community was defined. Within that community, small, relatively closed \npolicy making networks were found. To create these networks, influential actors \ncoalesced from across university departments and colleges, from government agencies \nand from the administration and faculty chambers. The emergent patterns and the \ncharacteristics of these influential relationships among key policy makers, including \ninstitutional and government actors, was described and interpreted to gain a greater \nunderstanding of the autonomy and capacity of these networks as they responded to the \npressing issue of teaching and technology in today's changing university. \nAnalysis of these policy networks and communities suggests that the policy issue of \nteaching and learning technology activated actors to form certain types of relationships. \nIn the Saskatchewan case, the network emerged because low capacity and low autonomy \nactors believed that the institution needed to be seen to be keeping up with technology. \nIn the Alberta case, the networks emerged because the actors believed that the institution \nhad to increase its market share. In all cases, the networks discovered were small and \nrelatively closed to the policy community. \nFurther interpretation found that in the Saskatchewan case, stable policy networks \norganized their interests objectively with the government in a weak and codependent \npressure pluralist network. In the Alberta case, policy networks were found to organize \ntheir interests more subjectively, creating a tight concertation network positioned to \ncapture targeted government funding. A comparison of the types of policy networks and \npolicy environments found that, though university faculty members have autonomy by \nAct and collective agreements, some networks chose to organize their interests \nhierarchically and to become codependent, while other networks maintained high \nautonomy and high capacity by exercising certain key policy development \ncharacteristics. \nIn all cases, the policy development process was found to be leaderless. The significance \nof the study is that this conceptual framework does provide university sector leadership \nscholars with an understanding of ill defined, pressing pan-institutional issue \norganization in large modern universities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it