The Identification of Stakeholders’ Living Contexts in Stakeholder Participation Data: A Semantic, Spatial and Temporal Analysis
Bibliographic record
Abstract
Stakeholders’ Participation (SP) aims to involve stakeholders in decision-making processes about significant choices affecting their organizations, cities, or communities. Stakeholders’ participation is maintained through SP processes (SPPs) that may be traditional (e.g., physical assemblies) or online (e.g., online forums). Whether traditional or online, the purpose of SPPs is to collect and analyze data in a way that it would bring a benefit to a given decision-making process. In SPPs, stakeholders try to communicate (a part of) their living contexts, i.e., to present their objectives, daily problems, intentions, and issues they are facing within their environment. A major challenge of decision-makers is then to ensure that the living contexts of stakeholders are considered in SPPs for an effective implementation of project and policies. This paper focuses on the specific issue of the “stakeholders’ living context identification” and attempts to account for how stakeholders implicitly identify their living contexts in their SP comments. Based on a qualitative analysis of SP data from four case studies in two countries, this paper identified a set of semantic, spatial, and temporal patterns allowing to capture the stakeholders’ living contexts in SPPs data. Moreover, a conceptual model emphasizing the importance for decision-makers to capture and understand semantic, spatial, and temporal dimensions in SPPs is proposed.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".