Engaging novice teachers in semiotic inquiry: considering the environmental messages of school learning settings
Bibliographic record
Abstract
Katherine Fogelberg’s insightful study of the messages of zoo signs describes the complex, sometimes contradictory nature of the messages they communicate. The construction and content of signs are influenced by institutional power. Fogelberg argues that the creation of zoo signage designed to inform the public can, through its messages, silence a perspective of care and compassion for animals. The research presented in the following article extends discussion about the value of critical considerations of cultural and institutional messages created and read in another type of setting designed to educate and inform, the school learning setting. The article reports on a project that engaged novice teachers in explorations of the nature and types of environmental messages found in learning settings. During our inquiry work together, novice teachers suggested areas of particular concern to them, and began to construct ideas about aspects of their work in which they plan to take action or engage in future inquiry. The research also reveals some of the challenges involved when novice educators first begin the process of engaging in semiotic interpretive readings of learning settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".