Read-aloud in preschool - more than mere pleasure? : On Change and Understanding among Teachers in relation to the Revision of Schools’ Teaching Programme
Bibliographic record
Abstract
The purpose of this study is to understand teachers' view of what function the reading-aloud of fiction serves in preschool. A recent event motivating such a focus is the Government's stated aim of promoting reading in preschools and a general emphasis on reading as a teaching tool, which has been clarified in the 2018 revision of the preschool curriculum. The result of my survey shows that more than half of the 368 participants are aware of or have heard about the Government's approach to reading, while just over a quarter of the participants do not know about it. However, most of them are positive about the school's clarification of reading as a teaching tool, regardless of whether they know about the initiative or not. According to the result, reading aloud is prioritized by the teachers themselves and just under half of the teachers make their choices based on the students' wishes. In those cases, it is most common that the teacher selects books recommended by teacher colleagues or other teachers on social media. The results also reveal that the stated purpose of reading aloud is to awaken the reading desire and gain a common language experience as well as to make the students interested in books. Here, the Swedish National Agency for Education emphasizes primarily the teachers' fourth option, namely reading as a pedagogical tool. Teachers also point out that the daily fruit session is well suited for taking time to read.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".