Interrogating the agency of non-profit labour market intermediaries: Casting light on ‘shadow spaces’ through an institutional-relational view
Bibliographic record
Abstract
In a context of government cutbacks, non-profit labour-market intermediaries are assuming a more significant role in efforts to combat precarious employment. Yet such organizations are still subject to state funding regimes, regulations, oversight and neoliberal logics. As such, some scholars argue that they constitute "shadow state" spaces. In this paper, we move beyond the 'shadow' concept, casting light on the ways that different state-non-profit relations shape non-profits' agency to define and realize their respective mandates. Building on a relational perspective, we hold that links between non-profits and the state are not linear. We complement this perspective with an institutional-relational approach to consider how a non-profit's distinct institutional configuration (i.e., regulations, funders, and partners) enables or forecloses agency vis-à-vis the state apparatus. Through an examination of two non-profit labour market intermediaries that serve immigrant workers in Montreal/Tio'tia:ke, our analysis lends insight into institutional elements that can enlarge a non-profit organization's space to maneuver.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".