Plural relational green space values for whom, when, and where? – A social media approach
Bibliographic record
Abstract
The values people ascribe to their interactions with and within the environment are essential to inform justice and sustainability transformations. The development of many of these values unfolds through enjoying so-called cultural ecosystem services (CES) such as outdoor recreation, landscape aesthetics or environmental education. A growing body of literature is improving the assessment of the multiple ways that people value human and non-human relations arising when enjoying CES. Yet, the geo-temporal-demographic patterns of values distribution and the lessons that can be derived are to be consistently analysed within this relational framework. Building on a visual and textual content analysis of social media (SM) data geotagged in a peri-urban park of Barcelona, Spain, this research explores the potential of analysing the associated metadata (such as geotag, timestamp and social media users' demographics – i.e., performed gender and residency) in order to develop a better understanding of the linkages between people's values and the situated context of their construction. Our results show trends in relational CES values distribution along and between the analysed spatial, temporal, and demographic dimensions. In particular, despite there being a multiplicity of values revealed across the whole case-study area, to enjoy contemplative CES, such as spiritual or cognitive value, people need to move away from highly frequented areas and prefer specific times of the day, respectively evening or afternoon. Locals show a higher preference to visit the park on weekends compared to non-locals, while women-performing users show a significantly higher drop in their CES benefits uptake compared to men-performing users at night. In addition to providing novel and fine-grained information for transformative practices toward justice and sustainability, this study highlights the importance of complementing CES studies employing SM with metadata analysis to improve our understanding of the relationship between the real and the more-than-real.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".