IN-HABIT Glossary (Annex to D5.1 Stakeholders Engagement with GDEI perspective Toolkit)
Bibliographic record
Abstract
The IN-HABIT glossary defines a shared vocabulary among the partners of the H2020 IN-HABIT project. It facilitates both the internal communication and cooperation among the partners during the implementation, and the external communication of its objectives and actions towards a wider audience. The glossary is an essential instrument to outreach diverse social, professional and cultural groups through different language environments, and is also part of the Dissemination, Exploitation, Communication and Outreach plan (DECO). The terms included in it have been mainly selected on the basis of the terminology employed by the submitted project description, completed with relevant terms that have emerged in the first phases of collaboration among the partners responsible for WP5, WP6, WP7 and WP8, andconsultation with the four cities (WP1-4).The definitions proposed here aim to circumscribe clear, shared, operational meanings of these terms within the specific objectives and practices promoted by this project. The additional purpose is to facilitate correct translations of the main language of the project into the four local languages, and to support simple and inclusive formulations of its key concepts for general non-expert audiences. This glossary is meant as a co-created common pool resource of IN-HABIT.The terms examined include: specific terminologies introduced by IN-HABIT methods and approaches; keywords widely used in EU policy and planning needing a clear explanation and communication to project participants; thematic keywords that have a specific relevance in disciplinary fields but may not be univocally recognised across different fields and to a general public; technical terms and acronyms.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.282 | 0.145 |
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".