An inquiry into the impact of institutional strategies on faculty partners' development of globally networked learning environments (GNLEs)
Bibliographic record
Abstract
This qualitative multi-case study inquired into how faculty partners negotiate their globally networked learning environments (GNLE) as they contend with their unique institutional contexts. This study investigated three cases of GNLEs, each with at least two faculty partners at different Higher Education Institutions (HEI)s from four countries: the United States, Canada, Ireland and Belarus. Data sets included participant interviews, institutional policy documents, institutional course documentation, and the shared online learning environment (SOLE). Using de Certeau's (1984) conception of strategies and tactics and further drawing on rhetorical genre and a critical technology theoretical lens, the study uncovered institutional strategies enacted within institutional document genres and embedded within technological codified constraints. Whilst some strategies enabled faculty partners' work, others constrained it; these constraining strategies were met with tactics as faculty partners worked to resist and, in de Certeau's words, "make do" (1984, p. 29). Key insights revealed that though faculty partners experienced a number of personal and professional benefits in their GNLE work, institutional strategies geared at global positioning both enabled and constrained their work. An example of an enabling strategy that was revealed was joint institutional funding for face-to-face meetings between faculty partners obtained through strategic internationalization policies. However, an example of a constraining strategy was the increased use of syllabus policies by university administrators to regulate the communicative practices of faculty. This has led to an administratively-driven purpose of the syllabus, thereby constraining its potential use to aid in faculty partner course negotiation and work. To contend with constraining institutional strategies, faculty used tactics that were diverse and creative. In one case, constraining strategies were governmental, and faculty partners responded tactically and courageously by smuggling books across a national border in the pursuit of academic freedom. This study contributes to the literature on globalization and higher educational institutions, globally networked learning environments, and the use of technology in higher education curricula. This study additionally provides policy considerations for HEI administrators aiming to position their institutions globally, providing a framework in which they may more consistently (and strategically) support GNLE faculty partner work and enhance globally networked course offerings within their curricula.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 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".