Where is Homelessness? When is Homelessness? Chronotopic Analysis of OECD Narratives of the Homelessness through Space, Time, and Body
Bibliographic record
Abstract
Defining homelessness clearly without reducing the problem's complexity helps governments frame effective and conscious policies. There is a growing need for a theoretical framework that explores the common ground and generative structure among broad narratives about homelessness. In this article, we propose that Bakhtin’s (1981) concept of chronotope has excellent potential to achieve this goal. Chronotopes help us understand how time, space, and body configurations are represented in language and discourse for recognizing various situations and personas. Chronotope also enables us to reveal the assumptions and perspectives behind the narrative. Using a chronotopic lens, we analyzed narrations of homelessness from national governments and international organizations of 38 Organization for Economic Co-operation and Development (OECD) member countries. The findings reveal the dynamism, diversity, and assortment of dimensions related to time, space, and body in the analyzed narratives and highlight five main perspectives viewing homelessness as an issue of security, vulnerability, quantification, inclusivity, and human rights. The results of this analysis will guide upcoming research in two primary areas: investigating varied measurement methodologies for assessing different aspects of time, space, and body and scrutinizing how distinct attitudes towards homelessness impact policymaking and development processes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".