How is the ecosystem services concept used as a tool to foster collaborative ecosystem governance? A systematic map protocol
Bibliographic record
Abstract
BACKGROUND: While the concept of ecosystem services has been widely adopted by scholars and increasingly used in policy and practice, there has been criticism of its usefulness to decision-makers. This systematic map will collect and analyse literature that frames ES as a collaboration tool, rather than as an ecosystem assessment tool, to answer the research question-how is the ecosystem services concept used as a tool to foster collaborative ecosystem governance and management? METHODS: We will search for publications using designated keywords in Web of Science Core Collection, Scopus, grey literature and conservation practitioner databases and websites. The search strategy aims to locate all ecosystem services studies related to collaboration and joint activities. After removing duplicates, we will screen papers in two stages-first by reviewing titles and abstracts and then by reviewing full text. Both stages will screen papers according to the following inclusion criteria: (1) the study is situated in the context of or related to environmental governance or management; (2) the study focuses on ecosystem services being used as a tool for collaboration; (3) the study describes a process resulting from applying the ecosystem services concept as a tool or approach; and (4) the ecosystem services concept is used in the study in a collaboration or group process in a substantial manner. We will exclude papers that do not address the ES concept as a process tool or approach or that use the ecosystem services concept to directly influence specific decisions or policy. Eligible studies will be critically appraised to assess their reporting quality. Studies will then be reviewed to determine: (a) the type of tool or mechanism that is the primary focus or example of the paper, (b) the rationale for using the ES concept, (c) whether a tool or approach was empirically tested in the study, (d) what the study found regarding the usefulness of ES as a tool or approach, and (e) any challenges to their use, if mentioned explicitly. A standard coding spreadsheet will be used by reviewers. Relevant metadata will be extracted for each paper assessed and used to construct an open-access online database. Finally, a narrative synthesis of metadata will be reported based on eligible studies.
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 | 0.183 | 0.243 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.039 | 0.025 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".