The Slow Professor: Challenging the Culture of Speed in the Academy, by Maggie Berg and Barbara K. Seeber. 2016. 115 pages, paperback. Toronto: University of Toronto Press.
Bibliographic record
Abstract
This paper explores the principles of the Slow Movement to counter work-stress among university and college teachers. We believe that a Slow approach to teaching and learning may be the most effective way to counter the erosion of humanistic education by the corporate ethos of consumerism, efficiency, accountability, and standardisation We explore the principles of Slow not only to counter the consumer model of education but also to foster better teachers and learners. It is well-documented that changes in academic work have created significant stress among academic teachers (Catano, Francis, Haines, Kirpalani, Shannon, Stringer, & Lozanksi, 2007; Miller, Buckholdt & Shaw, 2008), and students (Dabney, 1995; Brown & Ralph 1999; Rowbotham and Julian 2006), but what requires further attention is the link between the corporate reliance on efficiency and the problem of lack of time in learning and teaching. Corporatisation has sped up the clock. The Slow Movement—originating in the Slow Food Movement—has gained recognition as a way to resist both globalization and the frantic pace of contemporary life. While slowness has been lauded in architecture, business, urban life and interpersonal relations, among others, it has yet to be applied to academia. Yet, if there is one sector of society that should be cultivating deep thought in themselves and others it is academic teachers. The consumerism that has taken hold in higher education propels the belief that time is money, resulting in superficial learning (Coté & Allahar, 2011b; Readings, 1996). Perhaps the most damaging effect of corporatisation in the universities is that individual educators feel paralysed in the face of overwhelming odds. Our focus on individuals and their own professional practice is conceived as political resistance to corporatisation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".