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The present tense analyticisation process in brazilian portuguese

2023· dissertation· W4416173198 on OpenAlexfundno aff
Paulo Ângelo de Araújo Adriano

Bibliographic record

Venuenot available
Typedissertation
Language
FieldArts and Humanities
TopicLinguistics and Language Studies
Canadian institutionsnot available
FundersUniversidade Estadual PaulistaSociety for Research in Child DevelopmentUniversidade Federal do Rio de JaneiroUniversidade Nova de LisboaFundação de Amparo à Pesquisa do Estado de São PauloUniversity of OxfordUniversity of CambridgeUniversidade de São PauloUniversity of PennsylvaniaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversity of Southern CaliforniaUniversity of Ottawa
KeywordsProcess (computing)Brazilian PortuguesePortuguesePresent tense

Abstract

fetched live from OpenAlex

Ata da defesa, assinada pelos membros da Comissão Examinadora, consta no SIGA/Sistema de Fluxo de Dissertação/Tese e na Secretaria de Pós Graduação do IEL.I owe the completion of this PhD dissertation to numerous people.First and foremost, I express my heartfelt gratitude to my parents, Silvia and Donizete, who have been my pillars of strength since my childhood.I will forever cherish the memories of my childhood, when I dreamt of being a superhero to carry my mom on my back so we did not have to walk for long under the hot sun, a gas delivery man, a bus conductor, and a baker.Regardless of what I chose, I always knew I would have the support of the best parents I could ask for.I am deeply thankful that you believed in me unconditionally, invested in my education, and enrolled me in an English course that laid the foundation for my academic and professional success.I am deeply grateful for your constant support, encouragement, and sacrifices, without which I would not have achieved this milestone.Thank you for always calming me down with your wise words, reminding me that "in war, it's way worse".I also extend my thanks to my brother Pedro, Pietro, or Camaradinha, who has been an inspiration to me since elementary school.His academic brilliance and work ethic have motivated me to strive for excellence in my academic pursuits.To my beautiful and wise niece Milena, Mimi, or Princesa Milena, to whom I dedicate this PhD dissertation, thank you for reminding me of life's simple pleasures and for showing me that what really matters in life is to swap the World Cup cards that we drew with the finest artistic technique.I love you!To my extended family, thank you for your unwavering support and presence in my life.I express my sincere gratitude to Luiz Mendes, who has been my loyal companion during this intense journey, a great grammatica-lity judge, and a maths and statistics expert.Your friendship and consistent support have made this journey easier and more enjoyable.I'm grateful for your understanding of the long periods I spent in front of the computer talking to myself whilst writing this PhD dissertation.I am very lucky I have an amazing friend and partner like you.I also thank Lipp Luneta, who made me company when I could not solve a dilemma

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0040.006
Scholarly communication0.0060.008
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.027
GPT teacher head0.316
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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