Systemic evaluation of cross‐border networks of actors: Experience with a German‐Polish‐Czech cooperation project
Bibliographic record
Abstract
Cross‐border networks of actors constitute a special type of cross‐border cooperation as well as a special kind of network. This form of cooperation is characterized by a high degree of uncertainty, particularly in the case of the Polish‐German and the Czech‐German borders with their problematic history and rather weak traditions of cooperation. Evaluations can help to raise the effectiveness of cross‐border networks. This article refers to the concept of systemic evaluation. Such an evaluation is a collective process of learning and deliberation which is intended to increase the problem‐solving capacity of the system and to involve the participants and users from the beginning of the process. The main question dealt with in this article is how systemic evaluations of cross‐border networks of actors have to be designed and implemented. In the article, a brief survey on the state of art of systemic, participatory cross‐border evaluation is supplemented by a case study of the evaluation of the project Enlarge‐Net. The conclusions include the findings that systemic evaluations cannot be regarded separately from the intervention logic and that evaluators who are dealing with systemic evaluations of cross‐border networks of actors need a diverse tool box and have to adapt their methods to the actual phase of the cooperation.
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 | 0.040 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".