Making Sense from the In-Between State:Immigrants’ Identity Work & the Macro-Processes of Resistance
Bibliographic record
Abstract
This paper explores, the ‘in-between state,’ where the sensemaker tries to manage competing senses of a situation. We draw on the poststructural methodology of critical sensemaking (CSM) in the study of the micro-processes of identity work at play among a group of 19 Hong Kong Chinese skilled immigrants to Canada. Our findings reveals the confused and compromised experience of being an immigrant in the Canadian context. It illustrates the implications of the state of struggling in-between that was reportedly experienced by some informants – the (un)tangling processes they experience when searching for explanations and/or removing ambiguity in the sensemaking process. In short, this paper contributes to diversity management literature and practice through understanding immigrants’ identity construction, its oscillations, influences, and restrictions as agency in context. It helps diversity managers and policy makers to understand what is beneath the surface issue while provide social and structural support/strategies in workplace. For theory, this study reveals how power relations are shifted and resisted in the process. It bridges our understanding of how immigrant identity oscillates constantly in a state of in-betweenness. The changing sense of self also opens up a new way for immigrants to deal with structural constraints. This study enhances the relevance of critical sensemaking approach and analysis in doing empirical investigation in diversity scholarship.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".