Tapping into the ‘standing-reserve’: a comparative analysis of workers’ training programmes in Kolkata and Toronto
Bibliographic record
Abstract
This paper examines employment-related training programmes offered by state funded agencies and multinational corporations in Toronto (Canada) and Kolkata (India). In recent years both cities have witnessed a rise in the service sector industries aligned with global regimes of flexible work and the consequent reinvention of a worker subject that is no longer disciplined according to the needs of industrial production. A worker must now be self-regulated, competitive, flexible, with an ability to convey an urbane, English-speaking deportment within the workplace. Training of employees, especially soft skill training becomes crucial in this connection as a form of technology for achieving this end. Based on Martin Heidegger’s conceptualisation of ‘standing-reserve’, we suggest that what training programmes do in the context of neoliberal capitalist production is the creation of an essential quality of human-ness that has to be harnessed, its potentialities tapped and amplified through training. We further suggest that such programmes often remain heavily influenced by race/class/gender hierarchies as well as stereotypical assumptions of desirable/undesirable bodies, forms of socialisation and modes of habitation that often are naturalised in the course of training.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".