MétaCan
Menu
Back to cohort
Record W3094968376 · doi:10.21608/ijhms.2019.120016

All Mapped Out

2019· article· en· W3094968376 on OpenAlexaboutno aff
Alexandra Safir, Peter I. Schneider

Bibliographic record

VenueInternational Journal of Heritage and Museum Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsCultural heritageContext (archaeology)Space (punctuation)Quarter (Canadian coin)Identity (music)HistoryGeographyField (mathematics)Environmental ethicsAestheticsSociologyArchaeologyArtComputer science

Abstract

fetched live from OpenAlex

The monumental district of Florence/Italy and the workers’ quarter Kiel-Gaarden/Germany are two case studies on the use of mapping for storyscapes on different levels. Architectural heritage is most commonly understood to be representable and representative of a nation, city or cultural group. Not only in the aftermath of armed confrontation, is a re-assessment of what should be considered worthy of preservation and remembrance necessary, but it becomes perhaps a matter of every generation to re-evaluate of what matters in the built environment as part of an identity-shaping process. Part of this on-going process could be coming to terms with the historic past, and to include the forgotten aspects and events, which had shaped to some extent the lives of a group of people, communities or entire nations. In heritage studies, this contested heritage is known for instance as “shared heritage”, “uncomfortable heritage”, and places may be even described as “traumascapes”.3 What is known as the “spatial turn” occurred roughly 20 years ago.4 It is employed in the humanities where cartographic maps become a methodological tool. It is not a new approach to understand the historic context through space and in space, but it has undergone a re-launch because mass data can be processed and linked to a GIS. When this data becomes available online, transand interdisciplinary research is facilitated with a possibility to open up new perspectives and research fields. Especially in the area of architectural heritage, a field still largely dominated by expertism, this method may act as an inclusive and democratic tool for communities, which are not yet an integral part of a heritage discourse. It also provides an opportunity to integrate new heritage topics and places to be – if not preserved so at least – remembered. By visualizing for instance an urban space and the different places with a variety of functions and events, links between them can be revealed, which otherwise would have gone unnoticed. What has been termed in this context a “deep map” is “a finely detailed, multimedia depiction of place and the people, animals, and objects that exist within it, and are thus inseparable from the contours and rhythms of everyday life. Deep maps are not confined to the tangible or material, but include the discursive and ideological dimensions of place, the dreams, hopes, and fears of residents – they are, in short, positioned between matter and meanings. […] It is simultaneously a platform, a process, and a product. It is an environment embedded with tools to bring data into an explicit and direct relationship with space and time.”5 The relationship between space and human activity is well reflected in sociology stating that “space cannot be […] distinguished from society, but it is a specific form of society. Spatial structures are, just like temporal structures, forms of social structures”.6 That means that space and human activity constitute elements of heritage. At the same time, digitalisation has a decisive impact on the possibility for accessing primary data, for creating maps with this data, and to share these maps. Two case studies will demonstrate here how the map-based reconstruction of spatial activity helps to reveal different approaches to social realities. The case studies examine very different places and situations and will illustrate the potential of deep mapping to generate attention for hitherto neglected facets of life in urban spaces.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.494
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0040.001
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4940.282

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.

Opus teacher head0.141
GPT teacher head0.307
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Heritage and Museum StudiesSame topicCultural Heritage Management and PreservationFrench-language works237,207