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Record W6985002829

To Come Back Again

2015· dissertation· en· W6985002829 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2015
Typedissertation
Languageen
FieldComputer Science
TopicDigital Media and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)NothingAuntNatural (archaeology)Reflexive pronounGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

To Come Back Again is a bildungsroman of two characters – Luis and Rowena. The concept of leaving and returning (balikbayan in Filipino) is a major theme in the story. The novel details both characters’ perspectives: Luis’ reluctant departure from the Philippines juxtaposed with Rowena’s desperation to enact her own departure. Luis’ story starts when he is sent off to live with his aunt in Vancouver. He leaves behind a developing relationship with Rowena, which becomes the impetus for his longing to return to his former home. In Vancouver, he starts to question why he had to leave Cebu and to ascertain his purpose in staying in Vancouver. This anxiety manifests itself in a recurring nightmare. The pull of not only Rowena, but also his barangay (village), stifles any new relationships he forms. Despite his reluctance to entrench himself in Vancouver’s cultural milieu, and despite the fact that the city itself has begun to influence his way of thinking and speaking, Luis begins to question his identity, which then casts doubt on his goal of going back to Cebu. Rowena’s story begins a week after Luis’ departure. As Luis was her only ally and the one person she thought would change her life, his leaving forces Rowena to come to terms with her own situation. She comes to question the idea of permanence. Upon meeting Mr. Park (an older South Korean man vacationing in Cebu), Rowena sees a relationship with him as a way out of her poverty. Both stories explore the ideas of migration, poverty, class hierarchy – Filipinos vs. Chinese- Filipinos – and family responsibility.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: none
Teacher disagreement score0.578
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.042
GPT teacher head0.294
Teacher spread0.251 · 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
Published2015
Admission routes1
Has abstractyes

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