The temporalities of free knowledge work: Making time for media engagement
Bibliographic record
Abstract
This article situates media engagement as an under-examined form of knowledge work, offering a nuanced discussion of the temporalities of media work from the perspective of expert sources and contributors. Using in-depth interviews with expert women in Canada, we focus on the temporality of media engagement to understand the complexities of this labour—that it is often unpaid, ad hoc, and contingent. We offer three key findings: First, there is an ongoingness to media participation; preparation, training, and responding to comments are less visible forms of work beyond the obvious media contact. Unpacking the ongoingness of media engagement highlights the temporalities hidden within the extended present of media work. Second, contributors need to make time for this impromptu knowledge work, a complex process involving decisions about the value of each engagement. We argue that contributing to the media demands not only the knowledge work of being a source but also the labour to make and manage the time to contribute. Third, paying attention to the spacetimes of media engagement reveals the inequalities of this work. Contributing to the media often requires working beyond typical (paid) work hours and spaces, bringing additional burdens on women who do more caring and household labour. Examining the temporalities of media engagement as a form of invisible ‘free’ labour—and as a form of knowledge work that occurs inside other knowledge work—allows us to consider how work is changing in the new economy.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.046 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".