Playing With The Glocal Through Participatory e-Planning
Bibliographic record
Abstract
The application of ICTs has turned everyday life increasingly glocal with both positive and negative consequences. However, the local and the global are not polarities but interdependent categories representing multilayered space, which may be shaped, to a certain degree, through participatory e-planning. With participatory e-planning we mean the socio-cultural, ethical and political practice which takes place offline and online in the overlapping phases of the planning and decision–making cycle, by using digital and non-digital tools. But how does participatory e-planning that mainly serves the community, also help stakeholders to play with the glocal? The aim of the article is to present a set of examples from the Finnish context and to discuss, how community informatics may provide opportunities for stakeholders to deal with the glocal in the area of environmental improvement. On the basis of our comparative analyses and a case study in Finland, we claim that participatory e-planning enhances playing with the glocal, if certain technical, organizational and institutional capacities and a supportive infrastructure exist.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".