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Record W2483027944 · doi:10.1075/z.107.09jor

Some discourse patterns and signalling of theassessment-basis relation

2001· book-chapter· en· W2483027944 on OpenAlexaff
Michael P. Jordan

Bibliographic record

VenueJohn Benjamins Publishing Company eBooks · 2001
Typebook-chapter
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsQueen's University
Fundersnot available
KeywordsRelation (database)Basis (linear algebra)SignallingComputer scienceMathematicsMathematical economicsData miningGeometry

Abstract

fetched live from OpenAlex

Editors’ introduction As noted in the Introduction, we can focus on the patterns of text from two broad, related perspectives: in terms of repetition and of conjunction. A number of the papers in this collection combine these two perspectives, though often with a greater emphasis on one: for example, Darnton explores repetition, but in the context of Matching conjunctive relations, while Thompson & Thompson identify patterns of repetition primarily in order to discuss the conjunctive relations that they signal. Jordan concentrates solely on conjunction, since he is concerned with a relation which does not depend on repetition: that of Assessment and Basis. Assessment in Jordan’s terms is related to the concept of evaluation, the expression of an opinion about something, but is rather broader. An assessment may be made through thinking (making a deduction, or holding and/or expressing an opinion); but it may also be made through doing. An action taken in response to a situation or event constitutes an assessment of that situation. For example, if we see black clouds gathering as we sit down for a picnic in the countryside, we may assess it by thinking ‘It looks like rain’ or by expressing annoyance. But our assessment may equally take the form of getting out our umbrellas or abandoning the picnic. Thus the following three sentences all have two clauses in an Assessment-Basis relation, although the form of the assessment is clearly different in each case: Assessment Basis We predicted rain because it clouded over We were annoyed because it clouded over We went home because it clouded over Assessment–Basis is obviously close to a Cause–Effect relation, especially in cases like the third sentence (our going home was caused by the imminent rain); but Jordan argues that the most satisfactory way of distinguishing between them is in terms of conscious response vs. unwitting or mechanical outcome. Thus, in contrast to the examples of Assessment-Basis above, the following would be a Cause–Effect relation, since the temperature does not ‘decide’ to fall: Effect Cause The temperature fell because it clouded over. Basis in Jordan’s approach is generally straightforward: it is the reason or grounds for the Assessment. Jordan also outlines other elements that may appear in the text when an Assessment–Basis relation is constructed: the Topic (the entity, event or situation that is assessed — the weather in the examples above); and the Appraiser (the person who makes the Assessment — ‘we’ in the examples). Having established this broad, but internally coherent, definition of Assessment–Basis, Jordan then shows that it is a much more common and important means of linking chunks of text than might be expected — indeed, he argues that it can be seen as perhaps the most fundamental relation in any language. He explores in detail the ways in which the relation can combine with other types of conjunctive relations such as Cause–Effect, and can form part of larger patterns such as Problem–Solution. He also gives examples of chaining, where an Assessment–Basis can itself form the Basis for a further Assessment. He exemplifies different grammatical realisations of Assessment and Basis, and possible ways of signalling the relationship (though he points out that often there are no explicit signals). He discusses the various functions that Assessment–Basis can serve in text (e.g. how speakers may construct disagreements by expressing contradictory assessments and appealing to different bases); and he proposes that in order to understand more fully how the relation operates we need to look at issues such as what kinds of Basis are accepted or required in different contexts. It is interesting to consider the connections between the kind of text patterning that Jordan explores and those described by Hunston, by Thompson & Thompson and by Fries in their papers in this volume. Each deals with rather different kinds of patterning, though there are clear areas of overlap. Taken together, they indicate something of the complexity of the patterns of text; but they also reflect the variety of analytical approaches that have been developed to cope with this complexity.

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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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.293
Teacher spread0.255 · 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 designTheoretical or conceptual
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

Citations8
Published2001
Admission routes1
Has abstractyes

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