Toward a Sense of Place Unified Conceptual Framework Based on a Narrative Review: A Way of Feeding Place-Based GIS
Bibliographic record
Abstract
Space and place are two of the main concepts in several fields of knowledge, such as human geography, environmental psychology, urban sociology, architecture, urban planning, and others. Space is an objective and structured concept. It is mainly a physical location characterized by measured dimensions and geographical coordinates, while place is a location that holds meaning and value for an individual or a group, created through the human experience and social interactions. Sense of place is thus a set of precepted meanings and attitudinal ties toward a place (conative, affective, and cognitive bonds). From a geospatial perspective, the subjective aspect of sense of place is difficult to depict in a cartographic projection. From this angle, Place-Based Geographic Information Systems represent a set of initiatives that attempt to combine the objectivity of spaces and the subjectivity of places in digital systems, and that integrate spatial semantic characteristics as described by places’ users. In this paper, the methodological approach is mainly based on a systematic analysis and search of the scientific literature. It is a narrative review inspired and based on a scoping review strategy following the JBI methodology and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews) checklist. This bibliographic analysis is about understanding the characterization and the components of sense of place. Results take the form of a synthesis of the conceptual approaches most prominent in the literature, in addition to a conceptual model encompassing the full conceptual specificities of the sense of place concept.
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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 | 0.133 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.036 | 0.022 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".