Bibliographic record
Abstract
Finding Room in Beirut: Places of the Everyday demonstrates why it is worth our while to explore the value and contemporary meaning of urban areas about to undergo complete renewal. Branching off from discourses surrounding the terrain vague, the book argues that large populated urban areas meet the criteria of the vague and constitute a particular perspective from which to build a critical stance in regards to the contemporary city. But unlike a terrain vague, a vague urbain — inhabited areas where property ownership is usually obscure and informal behaviours a daily affair — possesses real communities and offers an alternative understanding on how a city can be practiced and how lessons should be learned before its complete transformation. Stemming from a photographic and architectural documentation of Bachoura, a central area of Beirut, Lebanon, the book shows how the vague urbain allows for different ways of inhabiting, ways that are as — or perhaps even more — real and anchored in the imagination of the city as those proposed by standardising developments. Building on the intricacies of found situations, improvised uses and local narratives, it is an exploration as to how the meeting of a marvellous realism with l’intrigue, the vague urbain, and temporary architecture can provide opportunities for the emergence of hidden narratives.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".