Spatial, temporal and semantic contextualization of citizen participation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Citizen participation (CP) aims to reinforce the engagement of citizens in decision-making processes about significant choices affecting their cities and communities. With the emergence of the web-based crowdsourcing model, participants have become more involved in electronic participation processes. However, according to the literature, CP processes are in some cases, disconnected from citizens' living context and lacking responsiveness. In this paper, we argue the relevance of context in citizen participation and we propose a conceptual model for opinion contextualization that is based on semantic, spatial and temporal dimensions. The contextualization aims to connect citizens' input to relevant contextual variables that would enhance the understanding of concerns and thus to increase participation processes responsiveness. In order to test the proposed approach, a qualitative analysis process was handled based on a random sample of public transportation data in a city in Canada. This study argues the relevance of considering spatial, temporal and semantic dimensions in citizen participation processes.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it