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Record W7070774328

Promoting physical inactivity and car dependence: the case of Waterford city’s suburbs

2020· other· en· W7070774328 on OpenAlexaboutno aff

Bibliographic record

VenueSETU Waterford Libraries - Open Access Repository · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationIrishPedestrianParaphrasePhysical activityQuarter (Canadian coin)TRIPS architectureUrban design
DOInot available

Abstract

fetched live from OpenAlex

Introduction To paraphrase the WHO GAPPA (2018), Irish citizens do not have access to safe places and spaces in their suburban communities in which to engage in regular physical activity (PA). Less than half of all Irish adults meet the recommended PA levels (46%: HI, 2018) and a meagre 17% of primary school and 10% of secondary school children do so (CSPPA, 2018). In addition, 74% of all journeys nationally are made by car (CSO, 2016), of which 26% are less than 2 km, a distance easily walked, and 57% are less than 8 km, a distance easily cycled; only 2% report cycling to school or work. None of this is surprising as the suburban environment, where the majority live, actively discourages PA for recreation or transport. We have, in fact, systematically designed physical activity out of our suburban areas because mobility, social connectivity and housing have not been planned together. Approach The DTTAS Design Manual for Urban Roads and Streets (DMURS, 2013 & 2019) is founded on four key principles: connected streets, multi-functional streets, pedestrian focus and multi-disciplinary approach. It acknowledged that the design of roads and streets in the past has prevented sustainable mobility, and, by inference, PA. However, despite DMURS applying equally to the suburbs, the guidance has not been applied here and car dependence continues to be built-in to the design of new residential areas, as the norm. This is euphemistically known as ‘carchitecture’ and takes the following form: 1. large, wide, open ‘distributor roads’ providing ‘free flow’ conditions for vehicles that segregate and separate residential areas; 2. single-entrance, cul-de-sac design housing estates that lack connectivity, permeability, and proximity to adjoining estates or any services at all, including public transport. Findings Such designs effectively prevent walking and cycling because destinations (friends’ houses, schools, shops, workplaces) are too far away, and the surrounding roads are full of traffic. So all residents are car dependent: they are left with no choice but to drive everywhere. This often prevents children from playing outdoors – because the street space is blocked with parked cars or dangerous because of moving cars. Such designs also lead to social isolation, as those without access to a private car (e.g., migrants, low SEG’s, young people) can struggle to access recreation facilities, employment and education. Young people in particular, can become entirely dependent on their parents to chauffer them everywhere. Social implications We have prioritised cars over people in Irish suburbs, to the detriment of the physical and social health of the people that live there. We cannot expect people to be physically active, whether for transport, or recreation, when inactivity is so strongly reinforced by the design of their environment. We are currently in the midst of a housing crisis, a gradually unfolding climate catastrophe and twin physical inactivity and obesity epidemics. It is essential, therefore, that we don’t try and fix the first problem by building more of what exacerbates the other ones.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.780

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.006
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.040
GPT teacher head0.322
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2020
Admission routes1
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