Sticky Campuses or Hybrid Hubs? Getting (Un)stuck in the Academic Workplace
Bibliographic record
Abstract
Continuing a trend that began before the global economic crisis of 2008, and extending through the COVID-19 pandemic period of 2020–2022, the academic labor market worldwide is marked by employment precarity that is related to the economic rationalizations of government austerity programs. Much has been said about the neoliberal academy and constraints facing faculty and staff as they attempt to do more with less. In recent years, so-called “World Class” universities and globalizing institutions have aimed to recover from the pandemic's negative impact on tuition and auxiliary services revenue by highlighting their “sticky campus” amenities to attract in-person student enrollments. Meanwhile, academic staff may prefer to think of their employing campuses as “hybrid hubs” that permit them to retain some of the work/life benefits they managed to achieve during the pandemic's work-from-home era. Students and academic employees may thus find themselves stuck between the push-pull of sticky campuses and hybrid opportunities. This chapter utilizes a comparative, visual approach to theorize “stickiness” and “hybridity” as relevant themes for the digital/physical campus in this transitional era.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".