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Record W1527157723 · doi:10.4225/75/57b289de40cd1

Zubulake: The Catalyst for Change in eDiscovery

2009· article· en· W1527157723 on OpenAlexaboutno aff
Penny Herickhoff, Vicki Luoma

Bibliographic record

VenueAustralasian Journal of Paramedicine · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCommon lawFederal Rules of Civil ProcedureElectronic dataPolitical scienceBusinessElectronic recordsLawLaw and economicsPublic relationsEconomicsCivil procedureComputer science

Abstract

fetched live from OpenAlex

Common law countries have been struggling with electronic data in regard to their discovery rules from the first digital document. All major common law countries, including Australia, New Zealand, Australia, United Kingdom, Canada, South Africa and the United States have recently changed their rules of discovery in an attempt to make sense of all this data and determine what, when and how data should be disclosed by parties in litigation. Case law in these countries has been defining the responsibilities of potential parties and attorneys to prepare for litigation that might happen. The case that was the catalyst of change was the 2003 United States case Zubulake v. UBS Warburg, LLC. Prior to this case judges and attorneys were trying to determine how to deal with electronic data that was becoming more voluminous. In this case, the court made a series of five pretrial orders concerning disputes over electronic discovery issues. These orders included defining accessible and inaccessible data, analyzing cost-shifting, and litigants’ duties to preserve electronic documents and consequences for failure to have an appropriate retention and deletion policy. This paper reviews the key aspects of the Zubulake case and examines the impact of the case on corporate record retention policies. This case was an important harbinger regarding how the discovery rules needed to be changed or redefined to accommodate the electronic data world.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.369
Teacher spread0.323 · 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 designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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