Bibliographic record
Abstract
Cultural mapping is a mode of inquiry and a methodological tool that aims to make visible the ways local stories, practices, relationships, memories, and rituals constitute places as meaningful locations. Although there is still a fuzziness to the boundaries of this field, cultural mapping has generally evolved along two main branches: The first begins with cultural assets, seeking to identify and document tangible and intangible assets of a place to ultimately develop a cultural resource or asset mapping. The second branch begins with a culturally sensitive humanistic approach, seeking to articulate a “sense of place,” people-place meanings, and distinctive elements. While the former approach tends to emphasize the documentation of “information” and the latter tends to focus more on “participation” and “meaning,” they are increasingly mutually informing approaches. Cultural mapping/cartography is allied with deep mapping, community mapping, participatory asset mapping, counter-mapping, qualitative GIS, and emotional mapping. These are connected through their focus on bottom-up processes for making visible the knowledge of citizens/residents, and shared topics of narratives, identity, histories, and local practices that bring meanings to places. Cultural mapping has shifted from focusing on tangible cultural assets to intangible aspects of place, aiming to discover what makes a place distinctive. Cultural products such as literature, film, and music draw from and contribute to the cultural meanings of a place; and the mapping of these onto a territory also forms a branch of cultural mapping. This work is also found within the fields of geography (see the separate Oxford Bibliographies in Geography articles “Geography and Literature,” “Geography and Film,” and “Geographies of Music, Sound, and Auditory Culture”), tourism, and digital humanities. Artist map traditions also influence the field of cultural mapping (see the section on “Map Art” within the separate Oxford Bibliographies in Geography article “Community Mapping”), with artists taking on a leadership role in many community-engaged cultural mapping initiatives. Cultural mapping has been co-developed through a loosely formulated international community of practice consisting of scholars, in-community practitioners, and policy/governance agencies (e.g., UNESCO, national cultural ministries, local authorities). While this annotated bibliography focuses mainly on the leading scholarly work in this field, it also provides an international selection of cultural mapping handbooks and toolkits as well as examples of cultural mapping projects. Following General Overviews, Special Journal Issues, Methodologies, and Theoretical Underpinnings of the field, this entry is organized according to seven domains of contemporary cultural mapping research and practice: Community Engagement, Participation, and Empowerment; Indigenous Cultural Mapping; Cultural and Creative Industries Mapping; Local Cultural Planning and Governance; Artistic Approaches; Literary and Film Mapping; and Technological Approaches.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".