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Record W2603122835

Ambiguity and the Poets

2008· article· en· W2603122835 on OpenAlexaboutno aff
Eleanor Cook

Bibliographic record

VenueConnotations · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCasualAmbiguityReputationOrder (exchange)Visitor patternSociologyLawHistoryComputer scienceLaw and economicsLinguisticsPhilosophyPolitical scienceBusiness
DOInot available

Abstract

fetched live from OpenAlex

A stranger meeting for first time might well be taken aback by her mixed reputation. She is disliked and avoided in some realms, whereas in others she is welcome. A philosopher like J. L. Austin will patrol streets of language in order to identify in his book, How To Do Things with Words. Ambiguity is bane of translators, who must decide whether it is intentional or merely casual, and ii casual, whether author is careless or lazy or ignorant. We do not want in legislation. Nor do we want it in our wills or in our financial affairs. (Lawyers, of course, like linguists, [consider] as productive because it triggers processes of disambiguation [Bauer par. 6]) Nor do we want in our traffic signs. A recent visitor from Australia, driving on express highway around Toronto, noticed signs for collector lanes. assumed - logically enough - that these were toll highways, collecting money, and so avoided them, overshot city, and was late for dinner. In fact, collector lanes simply siphon off - that is, collect - traffic that is preparing to exit.On other hand, is a useful and even welcome guest in some places. It is excellent device for concealing views. The oracles are said to have used regularly, though these turn out to be literary oracles more than historical ones, as far as we can tell. Macbeth's witches offer a well-known later example. The gods are prone to or amphibology, according to Chaucer's Criseyde: He hath not wel goddes understonde/ For goddes speken in amphibologies,/ And, for a sooth, they teUen twenty lyes [lies] (Troilus and Criseyde IV. 1405-07). In academic Ufe today, also has its uses. Suppose a selection committee for a senior position at your university receives a letter of recommendation on behalf of Professor X. How does it read sentence: You wUl be fortunate indeed if you can get Professor X to work for you. Intentional or not?For a literary scholar and critic, general dimensions of can appear singularly difficult to map. It seems to be not so much unknown land mass as a mythological creature, a Proteus, who changes shape whenever you wish to capture him - Proteus ambiguus, as Ovid calls him (Metamorphoses II.9). This many-sidedness is sometimes blamed on WUUam Empson's weU-known book, Types of Ambiguity, which pubUshed in 1930, in his twenties. Most of his examples are drawn from poetry. It is not a taxonomy, as one might expect from title. As his editor, John Haffenden, puts it: Seven Types of Ambiguity [...] offers less a methodology than Empson's own methodised briUiance (4).1 Pertinent criticism at time objected among other things that Empson [...] been too prodigal in his associative [...] interpretations, and that he too often worried parts without reference to whole (4). But term spread, thanks largely to so-caUed New Critics, though by 1947, one of them, Cleanth Brooks, wrote that held no brief for term ambiguity (or for paradox or irony): they are inadequate. Perhaps they are misleading. It is to be hoped in that case that we can eventuaUy improve upon them (195). By 1957, WUUam K. Wimsatt and Brooks acknowledged that the term 'ambiguity' was perhaps not altogether happy, for this term reflects point of view of expository prose, where one meaning, and only one meaning, is wanted (637). That is, norm for has always included what they call multiple impUcation (638) - a useful enough phrase, if clumsy. In 1958, Roman Jakobson accepted term ambiguity, defining it as an intrinsic, inaUenable character of any self-focussed message, briefly, a corollary feature of poetry (85). went on to quote Empson.2 (Jakobson' s essay, by way, was first pubUshed in English.) Meanwhile, Empson revised his book somewhat for later editions, then about 1973 mischievously wrote to a friend:Reviewers were teUing me, as soon as Ambiguity came out, that not aU was ambiguous, and I could see that method worked best where authors had had some impulse or need for process; but, as it had become my line, I went on slogging at it for two more books. …

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.061
Scholarly communication0.0130.015
Open science0.0010.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.040
GPT teacher head0.208
Teacher spread0.168 · 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.

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

Citations1
Published2008
Admission routes1
Has abstractyes

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