Affective Indignities at Work: Exposing the Inequality of Feelings in Canadian Professional Workplaces
Bibliographic record
Abstract
Despite the “talk” of diversity and inclusion, power in professional workplaces in Canada remains in the hands of a dominant white network. In order to devise effective strategies to achieve workplace equality, a deeper understanding of how power flows in professional workplaces is needed. In this thesis, I connect the framework of white institutional space, and the concept of feeling rules to explore how 20 foreign-trained South Asian (FTSA) women who occupied professional and/or managerial jobs in the Greater Toronto Area (GTA) conceptualized the power differences that produced and reproduced the form of workplace inequality they experienced. Based on a narrative analysis of the feeling rules experienced and navigated by FTSA women in the professional workplace, I conceptualize the workplace inequality they experienced in two interconnected ways to demonstrate the hegemonic nature of feelings in the professional workplace (i.e., the inequality of feelings). Firstly, through the structural lens afforded by connecting the concept of feeling rules and the framework of white institutional space, I argue that the cumulative impact of years of engaging in multidimensional emotional labour to constantly manage recurring racialized and gendered emotions is a form of workplace inequality. The emotions that FTSA women managed daily did not dissipate each day; instead they accumulated daily and as such are irreducible to invisible work (Daniels, 1987). Secondly, I use Ahmed’s (2004) critical concept of impression and the process of racialization to argue that the accumulation of racialized and gendered emotions through repeated performances of gratitude work, shame work, and empathy work, as directed by the five distinct feeling rules that FTSA women experienced–namely, to be hyper-pleasant, grateful, fearful, not angry, and not lonely–resulted in an intensification of emotions that produced affective associations that racialized their bodies. The impact of these affective associations on an FTSA woman’s bodily and embodied experiences in the workplace is a unique form of workplace inequality as it constrains her agentic actions. I have termed this form of workplace inequality affective indignities as it sustained the racialized relations of power in white institutional spaces that diminished the capacity of FTSA women to act.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.056 | 0.030 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".