Flows, Eddies, Swamps and Whirlpools: Inequality and the Experience of Work Change
Bibliographic record
Abstract
The main concern of this paper is how inequalities are implicated in the capacity individuals have to deal with changes in their work. The ability to deal with change - to seek it out, go with it, benefit from it - is a key aspect of neo-liberal discourse on the contemporary economy. Yet, there is little recognition within this discourse of the different capacities individuals have to initiate or respond to work change. This paper draws attention to such differences, and adds to arguments challenging the flexibilization and individualization encouraged by neo-liberal accounts of the economy. In particular, the paper highlights the two main strategies individuals are advised to adopt in managing employment change – lifelong learning and networking. We identify and provide paradigmatic examples of four profiles of work change: flows and eddies for the advantaged, swamps and whirlpools for the disadvantaged. Our research suggests that attention to how individuals negotiate changes in their employment will help to illuminate the dense and complex character of socially embedded work trajectories, as well as the intricate role of inequality in structuring the processes of change.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.046 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".