Bibliographic record
Abstract
This article describes the development of the Teaching and Learning Inquiry Framework (TLIF) and applications forits use. For decades teacher preparation and support has been dictated by a narrow mindset in which academicdisciplines have been taught in isolation. This landscape, however, is evolving to align with the view that the world israrely experienced in disciplinary silos. Interdisciplinary approaches to teaching and learning can enable students tomake more holistic connections to the world around them and be better prepared for college and career. With the recentpublication in the USA of four related standards-based reform documents across each of the core subject areas, teacherpreparation and professional development programs are evolving to offer teachers opportunities to examine theimplications of the new standards. To address these complexities, a guiding conceptual framework is needed thatfocuses in on how inquiry can serve as an entry point to frame the integration of content within and across disciplines.The TLIF was developed out of the hypothesis that teachers need to be prepared to teach in a more interdisciplinaryway using inquiry methods. There are six recursive stages to the TLIF: 1) stage and engage, 2) ask and pose, 3) planand monitor, 4) search and gather, 5) analyze and create, and 6) communicate and apply.
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 | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".