Claiming Places: An Exploration of People's Use of Locative Media and the Relationship to Sense of Place
Bibliographic record
Abstract
This dissertation explores the role of locative media in people’s place-making activities and sense of place. Sense of place is a human need that entails people’s meanings, memories, and feelings for a location. Recent technological and market developments have introduced powerful geographic information tools and place-related media. By identifying a user’s location, locative media deliver geographically relevant content that enable people to capture and preserve place information, virtually append it to space, and broadcast it to others. Despite locative media’s growing prominence, the influence on sense of place is not well understood. A major finding of this research is that use of locative media can contribute meaningfully to a person’s positive sense of place, including fostering existential connection. This study refutes scholarly and popular dismissals of the medium as only detracting from sense of place. Locative media was found to enable people to make spaces their own by offering geographic relevant information and experiences, recording and sharing place-related impressions, and presenting places in new and enjoyable ways, such as through defamiliarization and decommodification. This study demonstrates the importance of access to our hybrid spaces, unfettered by corporate restriction, to create meaningful place relationships. However, it was also found that locative media can distract from sense of place through the loss of serendipitous discovery. This study used qualitative field reports and semi-structured interviews with 22 people, predominantly from Ontario, Canada. Participants reported using 44 locative media applications in a variety of contexts and locations. Crawford’s urban counter dynamics (2012) and Bott’s sense of place work (2000) were employed as analytical frameworks. Methodologically, this study demonstrates the utility of Bott’s sense of place framework and provides an effective mix of methods for future studies. This research contributes to place theory and mobile media studies by examining the role of locative media in sense of place. From an information studies perspective, it offers evidence of the use and value of geographic relevance and vocality of information. Design guidelines are offered to aid the development of locative media to foster user engagement and conservation attitudes towards place.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".