Structures That Teach: Using a Semiotic Framework to Study the Environmental Messages of Learning Settings
Bibliographic record
Abstract
Everything within the learning setting holds the potential for learning and teaching. A significant, often overlooked source for accessing new information lies in the learner’s knowledge and use of cultural values, habits and norms. In addition to listening and reading texts, learning takes place through daily interaction with building and communication structures. These structures are representations of cultural values that are read by all who inhabit learning settings. They are structures that teach. The messages of these structures remain with students long after they leave learning settings. Like language, knowledge of culture serves as an everyday and ever-ready resource for information about how to gather and share knowledge and ideas about how learning proceeds. This article describes the value of documenting some of the environmental messages of these structures using a semiotic interpretive research approach. Semiotics explores the signs and systems of signification that are used to engage learners. Messages are organized and expanded using four main categories: 1) Architectural Messages; 2) Text and Curriculum Messages; 3) Social/Behavioral Messages and 4) Policy Messages. The study suggests that a semiotic consideration of learning settings allows identification and critique of ineffective environmental messages and suggests the creation of messages that will lead to more effective knowledge, habits and routines.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".