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Record W6993628163

Renting in Ireland

2014· article· en· W6993628163 on OpenAlexaboutno aff

Bibliographic record

VenueArrow - TU Dublin (Technological University Dublin) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsRentingAccommodationIrishQuarter (Canadian coin)PreferencePoliticsSpace (punctuation)Public housing
DOInot available

Abstract

fetched live from OpenAlex

As part of the overall housing sector, renting has seen a considerable increase in the first 14 years of the twenty-first century. Numbers renting are now similar to those of the 1950s, when Ireland was a very different place economically and socially. Today renting is driven by forces ranging from necessity to choice to ongoing urbanisation: it is becoming the tenure of preference for many, while remaining the tenure for others with no choice. Governing legislation, providers of rental accommodation and the various rental sectors’ economic value and importance are all in flux. The traditional divide between state-supplied social housing and the private rented sector is blurring in the face of political preference for market-led solutions and for the voluntary and private sectors to be the main, if not sole, providers of rental accommodation in Ireland.\nRenting in Ireland brings together for the first time a range of housing experts and practitioners to discuss and analyse renting’s role in Irish society. It comprises sections on the private rented sector; the social rented sector; and other relevant issues including renting and minorities, legislation, space standards and the experience in Northern Ireland. It is hoped that Renting in Ireland will help to contextualise discussions on renting, inform debate, and provide insight into how renting affects society and ideas on where to go next for a sector that has never quite received the attention it deserves.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.023
GPT teacher head0.183
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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