Coming into a sense of self in place though the networked eye of social/mobile photography
Bibliographic record
Abstract
The purpose of this paper is to examine the varied ways in which social media photography is being used in education, research, community, and art for locative meaning-making (Eernstman, 2013). Social media photography enables individuals to easily access and share personalized local information by enabling location-based information to be communicated visually through carefully crafted photographs, and searchable via hashtags (Author, 2016; Hochcman & Manovich, 2013). Thus, tools like Instagram are being employed in the creation of location-based mobile photography sharing communities across the globe, and are used by individuals to realize a sense of place. This paper provides a summary of several of the author’s research projects alongside a review of literature to illuminate some of the ways Instagram is used for local engagement and knowledge development by practitioners, researchers, and facilitators. These technologies are used in global communication, wayfinding, place-making, and establishing a sense of place–key to identifying with the local. As educational tools, social media photography platforms point to new methods for both international understanding and learning, as well as collaborative participant led learning based in daily life experiences, but pose risks associated with the sharing of personal information with corporate entities and online.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".