“What do you mean by that?”: solidarity in Canadian development practice
Bibliographic record
Abstract
Purpose With the launch of the Feminist International Assistance Policy (FIAP), the Canadian government named solidarity as a shared value and a driving motivation behind the FIAP. This paper explores how development workers understand and apply solidarity to their work, uncovering the opportunities and constraints they face. Design/methodology/approach In-depth, semi-structured interviews were conducted with 42 development workers from Canada’s federal development agency between 2019 and 2020. Transcribed data were coded by the author to identify how workers made sense of solidarity within the development industry. Findings The majority of workers were unsure of how to define or operationalize solidarity, demonstrating confusion. Commonality was routinely mentioned as a facet of solidarity, but workers understood this term in diverse ways, with some considering commonality as a precondition that inhibited a sense of solidarity with development partners in the global South due to differences in living conditions. About a quarter identified power and privilege as necessary considerations in the process of building solidarity, showing potential for bonds across the inequalities that define development. About 40% of workers identified the institutional structure of the organization as an obstacle to solidarity. Originality/value This paper presents original data from Canadian development workers, providing the first study of their understanding of solidarity as a development ethic. It shows the gaps between rhetoric and practice while recommending ways for development organizations to meaningfully engage with solidarity in their work.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.069 | 0.036 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".