Applying Semiotics & Systematic Visual-Textual Analysis to Racialized Transnational Carer Employees’ Arts-Based Data
Bibliographic record
Abstract
Due to increased migration and global aging, transnational caregiving plays an increasingly significant role in supporting work-family integration in Canadian society. Yet, there is limited research exploring racialized transnational carer employees’ (R TCEs’) experiences in Canada. TCEs are immigrants working in paid employment in Canada and providing unpaid care to family and/or friends across nations. This unpaid care can include emotional, physical and/or financial support. The data for this article were drawn from a larger study that examined R TCEs’ experience using arts-based and qualitative inquiry. Seventeen participants (male = 10, female = 7, other = 0) provided an art piece (e.g. poem, artifact, photograph, and drawing) as well as a written or verbal description of their piece’s meaning. This paper applies a semiotic framework and “Systematic Visual-Textual Analysis” to triangulate our analysis of participant art pieces and the meaning they gave to these creative products. Our analysis illustrates the multi-dimensional experience of transnational carer employees in Canada, through the common and overlapping symbolism of transition, care, love, and motivation. The research provides a cross-cultural, nuanced, and wholistic perspective on transnational care by R TCEs in Canada, while taking a novel analytical approach that allows for the systemic application of semiotics to arts-based analysis. Our findings have the potential to inform the implementation and content of caregiving supports in Canadian workplaces, post-secondary institutions, and medical care, as well as the application of semiotics and systematic visual-textual analysis in social science.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".