Bibliographic record
Abstract
This book presents a new approach to housing research, one that is relevant to all the social sciences. Housing research is diverse and operates across many disciplines, approaches and methods making collaboration difficult. This book outlines a methodological framework that enables researchers from many different fields to collaborate in solving complex and seemingly intractable housing problems. It shows how we can make progress in housing research and deliver better housing outcomes through an integrated approach. Drawing on the work of renowned Canadian methodologist, philosopher, theologian and economist, Bernard Lonergan (1904-1984), McNelis outlines a framework for collaborative research: Functional Collaboration. This new form of collaboration divides up the work of housing research into functional specialties. These distinguish eight inter-related questions that arise in the process of moving from the current housing situation through to providing practical advice to decision-makers. To answer each question a different method is required. Making progress in housing is the result of finding new answers to this complete set of eight inter-related questions. This approach to collaboration opens up a new discourse on method in housing and social research as well as new debates on progress and the nature of science.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".