Bibliographic record
Abstract
Aim: City&Co aimed to develop an innovative tool for a community-based, mixed validated quantitative and qualitative assessment of age-friendly cities, that can be used in multiple countries. This tool, called Geoportal, should help cities to plan for and implement age-friendly strategies using direct input from older citizens. Moreover, this tool itself has been developed with the active involvement of both older people and policy makers (city workers), in order to build capacity through local ecosystems at the city- and neighbourhood level. To provide guidance for the tool’s implementation and execution of plans, manuals and a manifesto have been published. Four cities were involved: The Hague in the Netherlands, Kraków and Wrocław in Poland and Bucharest in Romania. Methodology & data: The geoportal followed the user-centred design approach: a web-based solution for city workers, using Geographical Information Systems (GIS), with an integrated audit tool to be used by older citizens. We build on the quantifiable spatial indicators’ framework by Davern et al. [2020], which assists the assessment of age-friendly cities using a GIS methodology. In addition, outcomes are superimposed over socio-environmental vulnerability data from the 4 cities through a map overlay approach [Szewrański et al., 2018]. The integrated audit tool was co-created with older citizens from the four cities using multiple co-creation sessions. Multiple data used in the geoportal were collected through various methods: 1) Quantitative survey data of four AFCCQs, which were translated and validated using the Consensus-based Standards for Selection of Health Measurement Instruments, supplemented by additional questions on sustainability. Data were collected in four cities: The Hague, Kraków, Wrocław and Bucharest; 2) Qualitative pictures and comments tagged to a GPS location taken of public spaces and neighbourhoods by older people in the four cities using the geoportal over a three to six-month period, following the work by Barrie et al. [2019] and a community-based approach where older people are involved as citizen scientists. A hot spot analysis was made when evaluating the geoportal, showing the results on city maps. In addition, three co-creation sessions in the four cities were conducted, two with older people and one with older people and city workers together, to draft a strategic age-friendly agenda on evidence-informed policy and actions using the data collected in the project as content, resulting in a manifesto, published in four languages.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.911 | 0.835 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".