Climate services for adaptation : a qualitative user-provider analysis in Canada
Bibliographic record
Abstract
Climate services aim to inform adaptation to a warmer world, but their effectiveness depends not only on the data or information and how it is provided, but its interpretation and application in context. This qualitative study explores how climate service users and providers in Canada engage with long-term climate projections to support adaptation. Canada is an example of a mature and ongoing landscape of service delivery, contrasting with project-based services often presented in the literature. Thirty practitioner interviews were analyzed using Reflexive Thematic Analysis, with a focus on how understanding and use is shaped by context. Six themes, Digital, Relational, Credibility, Subjectivity, Transactional, and Systems, from the interviews, are conceptualized as Levers of Influence, positioned along a gradient from areas where climate services can act directly to support decision-making, to broader external and systemic constraints which require more indirect or positional approaches. Four key insights emerged: users are often decision-linkers rather than decision-makers; credibility substitutes for certainty in the face of uncertainty; relational work is foundational, not auxiliary; and service continuity reinforces traction. This study extends the climate services literature by illuminating how meaning is socially produced through practical judgements and organizational systems. The findings illustrate the value of ongoing climate services provision, such as those in Canada, as sustained infrastructure crucial for supporting meaningful climate adaptation over time, rather than as discrete interventions or technical fixes. The study ends by examining three cross-cutting areas suggested via the practitioner interviews, and explored as primers for further work. The areas contextualize an historic, a contemporary and a future friction: how climate services differ from weather services, how commercial actors are shaping services, and how artificial intelligence is poised to disrupt climate modelling and service delivery.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".