Pan-Arctic analysis of cultural ecosystem services using social media and automated content analysis
Bibliographic record
Abstract
Abstract In the Arctic, as in many parts of the world, interactions with the natural world are an important part of people’s experience and are often recorded in photographs. Emerging methods for automated content analysis of social media data offers opportunities to discover information on cultural ecosystem services from photographs across large samples of people and countries. We analysed over 800 000 Flickr photographs using Google’s Cloud Vision algorithm to identify the components of the natural environment most photographed and to map how and where different people interact with nature across eight Arctic countries. Almost all (91.1%) of users took one or more photographs of biotic nature, and such photos account for over half (53.2%) of Arctic photos on Flickr. We find that although the vast majority of Arctic human-nature interactions occur outside protected areas, people are slightly more likely to photograph nature inside protected areas after accounting for the low accessibility of Arctic protected areas. Wildlife photographers travel further from roads than people who take fewer photographs of wildlife, and people venture much further from roads inside protected areas. A large diversity of nature was reflected in the photographs, from mammals, birds, fish, fungi, plants and invertebrates, signalling an untapped potential to connect and engage people in the appreciation and conservation of the natural world. Our findings suggest that, despite limitations, automated content analysis can be a rapid and readily accessed source of data on how and where people interact with nature, and a large-scale method for assessing cultural ecosystem services across countries and cultures.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".