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Record W2980305565 · doi:10.1353/vpr.2019.0039

Zooming In and Out: Theories of Poetry from Checking the Periodical Poetry Index

2019· article· en· W2980305565 on OpenAlexvenueno aff
April Patrick

Bibliographic record

VenueVictorian periodicals review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryIndex (typography)Search engine indexingScholarshipComputer scienceProcess (computing)ZoomRaw dataLiteratureInformation retrievalWorld Wide WebArtLawProgramming language

Abstract

fetched live from OpenAlex

Zooming In and Out:Theories of Poetry from Checking the Periodical Poetry Index April Patrick (bio) As the intermediary stage in the Periodical Poetry Index process, checking bridges the input of data through indexing and its output in encoding. Our earliest conceptions of this step assumed it would require simply editing citations for accuracy and correcting any typos from the data entry process; however, in practice, checking has evolved into an activity that requires rethinking assumptions about this process and about the nature of poetry as it was published in nineteenth-century periodicals. Indeed, this iterative approach to the checking portion of Periodical Poetry has illuminated, among other trends, a surprisingly common practice of publishing poems in groups. From the earliest stages of this project, we recognized the importance of reviewing the data collected through the indexing process, and after mere months into our work together we had devised a circular approach where we each checked the poems indexed by one of our collaborators. Our initial goal for this stage was to ensure each entry included the correct bibliographic details, something we believed essential to creating a high-quality digital index; thus, we called the stage "checking," as if we were students reviewing the answers on a peer's exam. Other terms used for this type of work include "editing," which comes with implicit connections to textual scholarship and scholarly editions of a work, and "data cleaning," which as David Mimno observes, problematically implies "that there is some kind of pure or clean data buried in a thin layer of non-clean data, and that one need only hose the dataset off to reveal the hard porcelain underneath the muck."1 Katie Rawson and Trevor Muñoz consider the vagueness of the phrase, lamenting that "the specifics of 'data cleaning' are not described anywhere but reside in the general professional practices, materials, personal histories, and tools of the researchers."2 In response, [End Page 618] they suggest this "obscuring language … should be a strong invitation to scrutinize, perhaps reimagine, and almost certainly rename this part of our practice."3 In our approach to this part of the workflow, we have recognized the incredible value of an iterative approach in which we return to the periodical, just like the indexer in the previous stage. What We Mean by Checking Because it is not as commonly used to denote this sort of work, the term "checking" allows us to define what this part of our process includes. According to the Oxford English Dictionary, checking is "to control (a statement, account, etc.) by some method of comparison; to compare one account, observation, entry, etc., with another, or with certified data, with the object of ensuring accuracy and authenticity."4 Indeed, the purpose of ensuring accuracy was our primary intention for checking, and with that in mind, the work of checking seemed the mundane part of our workflow, essentially scanning a spreadsheet of poems for errors and making small adjustments to commas in the first lines or correcting places where the poet's printed initials were transposed in indexing. In practice, however, the checking stage requires balancing both closer and more distant views of the data, not only looking at the page of the periodical to confirm the indexed entry is correct but also considering the trends and connections that appear across the larger collection of poems. In this stage we make sense of discoveries about our data. Throughout our various rounds of checking, we have always focused on correcting any minor errors from the indexing process. During the checking process, we also update entries based on changes to our data collection categories, such as poem length, which we adjusted after realizing we needed to account for very long poems over one hundred lines. For example, in 2014 we began rechecking items that had been checked in 2011, so the entries needed poet and signature gender, updates to the number of lines, and changes to the title fields. The multiple spreadsheets of poems indexed from the early decades of Blackwood's, 1817 to 1845, included 1,544 poems. To streamline the process of checking, the entries were consolidated into a master spreadsheet and sorted...

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)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.005
Science and technology studies0.0070.056
Scholarly communication0.0120.031
Open science0.0020.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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