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Record W4236561953 · doi:10.1145/1463788

Proceedings of the 2008 conference of the center for advanced studies on collaborative research meeting of minds - CASCON '08

2008· paratext· en· W4236561953 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsIBMSession (web analytics)Library scienceGovernment (linguistics)Variety (cybernetics)Computer scienceResearch centerPublishingPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Welcome to CASCON 2008 --- the 18th Annual International Conference hosted by the IBM Centers for Advanced Studies. The CASCON "Meeting of Minds" conference series provides computer science and software engineering academics and professionals the opportunity to explore some of the exciting research that is underway in Canada and around the world. This year we had many interesting submissions that describe innovative approaches and fresh perspectives. CASCON 2008 attracted 73 submissions of papers from a wide variety of places. Of the 208 authors who were listed on the submitted papers, 128 (62%) came from Canada, 28 (13%) from Europe, 27 (13%) from Asia, 18 (9%) from the US, and 7 (3%) from South America. Measuring by institutions, 158 (76%) of the authors were from academic or government research institutes and 50 (24%) from industry. We followed a rigorous process to ensure that accepted papers met a standard of high quality. Each paper was reviewed by at least three members of our Program Committee, followed by a three-week on-line discussion period. Ultimately, we accepted 23 papers (32% acceptance rate) and recommended to authors of 14 papers to submit their work to the Posters track. The resulting program features three sessions on Software Engineering, two sessions on Systems, and a session each on Web Applications, Databases and Compilers. We hope you will take the opportunity to attend the Papers sessions following the morning keynotes.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.106
GPT teacher head0.381
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2008
Admission routes1
Has abstractyes

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