Bibliographic record
Abstract
Fly-in fly-out (FIFO) work camps are built and organized to ensure that long-distance rotational workers are fed, housed, and mobilized in sync with the pressing yet unpredictable rhythms of resource extraction. Positioned thus 'betwixt and between' the complex relations of work and life (Johnsen and Sørensen, 2015), the work camp is a generative yet hitherto neglected example of the temporal operations of permanent liminality (Bamber et al., 2017). But what does this mean for workers? If camp does the liminal work of managing the temporal challenges of the resource-based mobility regime, how do FIFO workers experience and respond to its inevitable lived consequences? Drawing on rare qualitative fieldwork in Canada's Athabasca Oil Sands, we explain the effects of camp time-disorientation, monotony, and entrapment-and examine the temporal tactics workers deploy to manage those effects, from embracing and disrupting internal camp routines to aligning and syncing with outside and future-oriented temporalities. We argue that workers' tactics make them 'competent liminars' (Borg and Söderlund, 2015) of camp time, which is, in turn, crucial to the latter's disciplining function within the FIFO mobility regime. Our findings invite renewed attention to the temporal mediation accomplished by liminal people and places, especially in organizational contexts aimed at institutionally harnessing social time to productive imperatives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".