Volunteerism in Context: A Comparison on Habitat for Humanity Canada Programs
Bibliographic record
Abstract
This thesis applies a Bourdieuian analysis to a qualitative study of volunteerism that focuses on the question of how ‘doing good’ relates to social change overall? - in particular, the ways that social class influences the volunteer experience and the overall culture of volunteerism. I argue that the leading theoretical research models of volunteerism need to recognize the evolving nature of the activity and theorize the influences that are structuring the culture of volunteerism. What emerges is a model of volunteering where motivation is understood as a complex set of factors that are structured by social class identities and volunteering is understood as a form of distinction that can be used to acquire cultural capital. Social class-based ideas, in particular, the values related to the middle class, have become a part of the culture of volunteerism and, in part, create and reproduce the social change/volunteerism paradox. The volunteerism/social change paradox is the idea that volunteerism is often perceived as a social change activity when in many cases it reproduced the status quo. Without a strong activism component to the volunteerism, it is not an inequality challenging activity. This thesis presents interview and observation data collected with Habitat for Humanity Canada in their two largest programs – the National program (domestic) and the Global Village program (international). By utilizing ideas of class, class distinction and social and cultural capital from Bourdieu’s work, the role of class, the culture of benevolence (or volunteerism) can be explored in a new way. What emerges is a culture of volunteerism that is deeply influenced by middle class values where social change ideas are common but structural change is not – resulting in the volunteer/social change paradox.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".