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Record W3161097610 · doi:10.15476/elte2019.112

On the Road with Kālidāsa: Countries, Cities and Sacred Places

2019· dissertation· en· W3161097610 on OpenAlexfundno aff
Péter Száler

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsnot available
FundersEötvös Loránd TudományegyetemEmberi Eroforrások MinisztériumaYork University
KeywordsGeographyArt

Abstract

fetched live from OpenAlex

Recently, however, a couple of scholars called attention to another basic characteristic of space.According to them, space cannot be regarded as "a neutral box, in which historical actions take place", 2 but it is rather a human construction developed under (perhaps) social expectations. 3 Actually, these two attitudes suggest a differentiation between Space and spaces.Space in itself is an abstract entity, about which, though, we have experiences, we are yet unable to comprehend it in its totality.The several societies, cultures and individuals, therefore, construct spaces with the help of their spatial experiences.Spaces are well-built systems, which provide models of the incomprehensible Space.Spaces, unlike Space, are human products, and therefore, they can be represented.The most typical means of this is the map.The main goal of the map is objectivity, due to which it adopts a kind of God's view. 4 However, because the object of the map is space (not Space), maps never become absolutely objective.In connection with this, we should think of the modern maps of the World.In Europe, usually, we consider it normal that the European continent takes place in the middle of such maps, but we are surprised if we see an Australian map, in which Australia occupies the same position.In this way, maps, just as spaces, though they work towards objectivity, never get rid of subjectivity completely.After transforming Space into space, there ordinarily occurs the "conquest of the space".This means that the societies, cultures and individuals distinguish places within space.Following Yi-Fu Tuan's definition, if space becomes better known and associated with values, it transforms into place. 5 Here, we also arrive at the main subject of this dissertation, which is Klidsa's landscapes.What do one's landscapes tell us at all?First, the landscape (or landscape painting) is the typical genre of the place.They, more accurately, uncover the means by which the poet creates places in the homogeneity of the space.Landscapes, therefore, differ from maps focusing mainly on space.They are organised around arbitrary focal points, 6 as a result of which they, intentionally, abandon the general objectivity.Because their object is always a momentary

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.850
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.014
GPT teacher head0.198
Teacher spread0.184 · 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 teacher head, 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

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