Fast Professors, Research Funding, and the Figured Worlds of Mid-Career Ontario Academics
Bibliographic record
Abstract
Heightened pressures to publish prolifically and secure external funding stand in stark contrast with the slow scholarship movement. This article explores ways in which research funding expectations permeate the “figured worlds” of 16 mid-career academics in education, social work, sociology, and geography in 7 universities in Ontario, Canada. Participants demonstrated a steady record of research accomplishment and a commitment to social justice in their work. The analysis identified four themes related to the competing pressures these academics described in their day-to-day lives: getting funded; life gets in the way; work gets in the way; and being a fast professor. Participants spoke about their research funding achievements and struggles. In some cases, they explained how their positioning, including gender and race, might have affected their research production, compared to colleagues positioned differently. Their social justice research is funded, but some suspect at a lower level than colleagues studying conventional topics. In aiming for the impossible standards of a continuously successful research record, these individuals worked “all the time.” Advocates claim that slow scholarship is not really about going slower, but about maintaining quality and caring in one’s work, yet participants’ accounts suggest they have few options other than to perform as “fast professors.” At mid-career, they question whether and how they can keep up this pace for 20 or more years.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.033 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".