MétaCan
Menu
Back to cohort
Record W220455632

A War of Words: Software Programs Developed to Combat the Scourge of Student Plagiarism Have Found Opposition from the Very Circle of Educators They're Meant to Help

2007· article· en· W220455632 on OpenAlexaboutno aff
J. W. Paterson

Bibliographic record

VenueT.H.E. Journal Technological Horizons in Education · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)EncyclopediaClass (philosophy)Mathematics educationSociologyMedia studiesComputer scienceLawPsychologyLibrary sciencePolitical scienceArtificial intelligencePolitics
DOInot available

Abstract

fetched live from OpenAlex

THERE WAS SOMETHING DIFFERENT about the batch of note cards Maribeth Mohan received this past year from the students in her senior composition class. They weren't ragged or wrapped in rubber bands, or half-filled with chicken scratch, lifted word for word from an encyclopedia, the way they'd been so often in the past. Instead they were neat and well organized, with the original material presented alongside the students' own, paraphrased thoughts. After 34 years of messing with lost or incomplete note cards, rubber-banded together and very disorganized--and plagiarism issues of all sorts--this was a real breath of fresh air, says Mohan, a teacher at Glenbard High School in Glen Ellyn, IL. The difference between last year's class and ones previous? Last year's group of students used PaperToolsPro (www.papertoo/spro.com), one in a torrent of software programs created over the last 10 years to fight the internet-energized plagiarism epidemic that has infected schools. Mohan says PTP helps her encourage students to use proper research techniques and shows them plainly the difference between plagiarizing and paraphrasing. The software enables students to search for material in an organized fashion and guides them through the process of citing sources, allowing them to assemble their research on note cards that can be sorted various ways and then placed into a word processing document. [ILLUSTRATION OMITTED] Mohan's approach is a proactive measure to ward off student as opposed to the more reactionary applications that have found a number of opponents among the very population they purport to help: educators. One of those unlikely critics is Charlie Lowe, a writing professor at Grand Valley State University near Grand Rapids, MI, and spokesperson for the 6,000-member Conference on College Composition and Communication (www.ccccip.org), which has published a position paper critical of anti-plagiarism programs. CCCC is among those arguing that the spate of new plagiarism detectors creates the wrong atmosphere for writing, finds a student guilty until proven innocent, and infringes on student rights. The group is especially opposed to products that put student papers in a database to be compared against others, such as iParadigms' (www.iparadigms.com) popular Turnitin.com, which acquires and inspects student work. We have to teach students about plagiarism, Lowe says, but if all we do is catch them without taking responsibility for the process, how do they learn about the proper use of research material? Technology is no substitute for good Lowe questions the ethics of releasing student essays to a business that then uses them against other students. We are asking students to give their text to these companies so the companies can make money off it, he says. Students at Virginia's McLean High School are of a similar mind as the professor. Last fall they collected about 1,200 names on a petition protesting the use of Turnitin because they said it infringed on their intellectual property rights and assumed guilt. Users should also realize that anti-plagiarism programs have their shortcomings. They may not check books or subscription services; they don't detect plagiary of ideas; and they can give false positives or, conversely, clear a paper without any real assurance that nothing has been copied. Some critics also suggest that, in time, students simply will find ways around them. Russ Hunt, a professor at Canada's St. Thomas University in New Brunswick, and an outspoken opponent of plagiarism detection tools, imagines a program that runs a thesaurus through an existing text, making enough word substitutions that the cheating would be undetectable by essay-comparison software. How hard is that? Hunt says. surprised I'm not already seeing it advertised. Rebecca Moore Howard, associate professor of writing and rhetoric at Syracuse University, says that detection applications are more about policing than teaching. …

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaResearch integrity
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablemedium
gptResearch integrity
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.356
Teacher spread0.318 · 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

Labeled directly by 2 models reading the full record.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueT.H.E. Journal Technological Horizons in EducationSame topicAcademic integrity and plagiarismCategoryResearch integrityFrench-language works237,207