Experiences working split shifts: a phenomenological study of early childhood educators
Bibliographic record
Abstract
Care work in general, and the work of early childhood educators, in particular, are both undervalued and gendered (Langford, Richardson, Albanese, Bezanson, Prentice, and White, 2017). While there is substantial research outlining the low wages and undervaluation of Ontario’s Registered Early Childhood Educators (RECEs), there is no known research on the effects that split shifts have on workers in this sector. Split shifts, when viewed by a worker as problematic, were found to be detrimental to overall health in the case of bus drivers (Ihlström, Kecklund, and Anund, 2017). It is not known whether the same could be said for RECEs. Given this noted gap, there is a need for research on the impact of working split shifts on RECEs. This study aims to address this noted absence of research for this female-dominated workforce performing care work. In order to do so, ten RECEs who self-report working a split shift were asked about their experiences through in-person, semi-structured interviews. These interviews gathered insight on how RECEs perceive that this work arrangement affected them professionally and personally, as well as what they believed could be done to address this scheduling system. Some of the key findings were that working split shifts resulted in even lower than “normal” compensation, and a sense that RECEs were being policed. There were also concerns about space and language indicating ownership of classroom space, as well as challenges navigating territoriality around that space. Finally, there was an overall feeling that split shifts helped to further undervalue the undervalued RECEs in general.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".