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Record W4200053492 · doi:10.26721/spafa.pqcnu8815a-09

Before Bagan: Using Archaeological Data Sets to Assess the Traditional Historical Narrative | ပဂမတငမကလ၏အစဉအလသမငအဆအမနမက ရရငသရတသ နပညပဆငအခကအလကမအသပပ၍ဆနစစပခင

2021· article· my· W4200053492 on OpenAlexafffund
Scott Macrae, Gyles Iannone, Kong Cheong

Bibliographic record

Venuenot available
Typearticle
Languagemy
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsTrent University
FundersSocial Sciences and Humanities Research Council of CanadaTrent University
KeywordsNarrativeSettlement (finance)ExcavationPresentation (obstetrics)HistoryArchaeologyScale (ratio)Computer scienceGeographyArtLiteratureCartographyWorld Wide WebMedicine

Abstract

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What we know about Bagan derives almost exclusively from historical sources – namely retrospective chronicles, inscriptions, and changing architectural styles. To date, archaeological excavations have played a limited role in augmenting or challenging this traditional narrative. This is unfortunate, because small scale excavations within Bagan’s peri-urban settlement zone, and within the walled and moated “royal city,” have demonstrated considerable knowledge about the city’s past. This is especially true for the Pre-Bagan phase (600-1044 CE). This presentation documents what we think we know about the time “before Bagan,” using the established sources, and assesses this narrative using information from contemporaneous excavation levels. ပုဂံခေတ်ယဉ်ချေးမှုအခြျာင်းျို သမိုင်းအေေျ်လျ်မေားဖြစ်သည့် အစဉ်အလာရာဇဝင်မှတ်တမ်းမေား၊ ချောျ်စာမေား၊ နှင့် ခဖပာင်းလဲလာေဲ့သည့်ဗိသုျာပုံ စံမေားမှသာလေင် သိြျရသည်။ နှစ်သျ်တမ်း သတ်မှတ်ရန်အတွျ် ခရှးခောင်းသုခတ သနဆိုင်ရာတူးခြာ်ခလ့လာမှုမေားသည်အစဉ်အလာအဆိုအမိန့် မေားျို ခဝြန်စစ်ခဆးရန် (သို့) ဖပင်ဆင်ြျရန် လုံခလာျ်မှုမရှိြျခသးခေေ။ ပုဂံမမို့ရိုး၊ ျေုံးဧရိယာနှင့် မမို့အစွန်အြေ ားခနရာမေားတွင်ခလ့လာေဲ့သည့် အနည်းငယ်မျှခသာ စမ်းသပ်တူးခြာ်ခလ့လာမှုမေားျ ပုဂံမမို့၏အတိတ်ျာလျို သိရှိနိုင်ခစရန် ရုပ်လုံးခြာ်ြပခနြျသည်။ ပုဂံမမို့ဖပမတိုင်မီျာလ (၆၀၀-၁၀၄၄ စီအီး)နှစ်သျ်တမ်းတွျ်ေေျ်မှုအခဖြမေားရရှိေဲ့သည်။ ယေုတင်ဖပမည့် စာတမ်းမှာ ပုဂံခေတ် မတိုင်မီျာလအခြျာင်းအရာမေားျို ခရှးခောင်းသုခတသနပ ညာရပ်ဆိုင်ရာတူးခြာ်မှုရလဒ်မေားနှင့် အစဉ်အလာအဆိုအမိန့်အေေျ်အလျ်မေားျို စစ်ခဆးအသုံးဖပုလေျ် မည်ျဲ့သို့ခတွးခတာသိရှိလာနိုင် ခြျာင်းျို တင်ဖပမည်ဖြစ်ပါသည်။

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.439
GPT teacher head0.404
Teacher spread0.035 · 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
GenreEmpirical

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
Published2021
Admission routes2
Has abstractyes

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