Construct · 4 fields · 10 citations

Where does recall collide?

One name. Different field. Different construct. One map.

● jingle · one word, four constructs Nearest construct by meaningforgettingcompare the meanings
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Every construct on the map is compared with every other by the meaning of its definitions, and the closest one is surfaced here. The nearest construct to recall is forgetting.

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KENT 1955 → CONFUSION MATRIXFREE RECALL → UNAIDED RECALLrecall4 CONSTRUCTSRetrieval & reviewKENT 1955 · DA SILVA MOOREMachine learningPOWERS · SAITOPsychologyTULVING · MURDOCKAdvertisingWELLS · DAY-AFTER

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2026

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Information retrieval and document review

The fraction of relevant documents a search found

The proportion of all relevant documents in a collection that a search or review actually retrieved. Kent, Berry, Luehrs, and Perry proposed the measure in 1955, alongside what became precision, as the operational criteria for judging a retrieval system. The pair became the yardstick of document review in litigation when Grossman and Cormack showed in 2011 that technology-assisted review reached recall at least as high as exhaustive manual review at a fraction of the effort, fixed the vocabulary in their 2013 glossary, and Da Silva Moore accepted the method in 2012 with recall as the number the parties argued about. Rio Tinto confirmed in 2015 that the method no longer needed defending, only its validation. Cormack and Grossman's 2014 SIGIR study compared training protocols on that criterion. Recall here is a completeness rate estimated by sampling against a relevance standard, and it is the measure a court is asked to accept as proof that a production is reasonable.[1][2][3]

Unit
A set of retrieved documents against the set of relevant ones
Recovery means
Not applicable, the criterion is the estimated fraction retrieved, with a confidence interval
Measured by
Sampling of the unreviewed remainder against assessor relevance judgments; recall at a stopping point

Machine learning and statistics

the proportion of actual positives a classifier labels positive: true positives over true positives plus false negatives.

The proportion of actual positives a classifier labels positive: true positives over true positives plus false negatives. It is the same number epidemiology calls sensitivity and signal detection calls the hit rate, and Powers's 2011 survey traced the several names and showed how recall, precision, F-measure, and ROC analysis relate and where each misleads. Saito and Rehmsmeier showed in 2015 that with imbalanced classes the ROC curve, which plots recall against the false positive rate, can look excellent while the precision–recall curve reveals that most positive predictions are wrong. Recall here is one cell-ratio of a confusion matrix, defined for any binary decision, and it is traded against precision by moving a threshold.[4][5]

Unit
A classifier's decisions over a labelled test set
Recovery means
Not applicable
Measured by
True positive rate at a threshold; area under the precision–recall curve

Cognitive psychology

Retrieving an item from memory without it being presented

The production from memory of material learned earlier, in contrast to recognition, where the material is presented and the person judges whether it was seen. Ebbinghaus began the experimental study in 1885 with nonsense syllables and savings scores. Murdock reported in 1962 the serial position curve of free recall: the first and last items of a list are recalled best, and the recency part vanishes with a delay. Tulving and Pearlstone showed in 1966 that giving category cues at test recovered words that free recall had missed, and drew the distinction between what is available in memory and what is accessible at the moment of retrieval. Recall here is a performance of an individual on a test, and its interest is in what the failures reveal about how memory is stored.[6][7][8]

Unit
An individual's output on a memory test
Recovery means
Retrieval of an item that was available but inaccessible, typically through a cue
Measured by
Proportion of studied items produced in free, cued, or serial recall

Advertising research

a copy-testing measure of whether people exposed to an advertisement can report it later, aided or unaided.

A copy-testing measure of whether people who were exposed to an advertisement can report it later, unaided if they volunteer the brand when asked about the product category, aided if the brand is named. Day-after recall, the surveying of viewers the day after a broadcast, was the industry standard for television for decades. Singh, Rothschild, and Churchill compared recall and recognition as measures of forgetting in 1988 and found they decayed at different rates and answered different questions. Wells's 2000 review set recall beside recognition and rating scales, and argued that each captured a different aspect of an advertisement's effect and that none was the effect itself. Recall here is a survey statistic about an audience, used to decide whether a campaign runs, and it borrows the psychology laboratory's word for a commercial purpose.[9][10]

Unit
An advertisement's audience, surveyed after exposure
Recovery means
Not applicable, the criterion is the share of the audience that remembers
Measured by
Percentage aided and unaided recall in day-after or in-market surveys

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Nearby collisions

relevance6 fields · 13 citationsjingle · one name, six constructslearning6 fields · 14 citationsjingle · one name, six constructsvalidity6 fields · 10 citationsjingle · one name, six constructs

References

  1. Kent, A., Berry, M. M., Luehrs, F. U., & Perry, J. W. (1955). Machine literature searching VIII. Operational criteria for designing information retrieval systems. American Documentation, 6(2), 93–101. https://doi.org/10.1002/asi.5090060209
  2. Grossman, M. R., & Cormack, G. V. (2013). The Grossman-Cormack glossary of technology-assisted review, with foreword by John M. Facciola, U.S. Magistrate Judge. Federal Courts Law Review, 7(1), 1–34.
  3. Cormack, G. V., & Grossman, M. R. (2014). Evaluation of machine-learning protocols for technology-assisted review in electronic discovery. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 153–162). ACM. https://doi.org/10.1145/2600428.2609601
  4. Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37–63. arXiv:2010.16061 https://doi.org/10.48550/arXiv.2010.16061
  5. Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10(3), e0118432. https://doi.org/10.1371/journal.pone.0118432
  6. Ebbinghaus, H. (1885). Über das Gedächtnis: Untersuchungen zur experimentellen Psychologie. Duncker & Humblot.
  7. Murdock, B. B. (1962). The serial position effect of free recall. Journal of Experimental Psychology, 64(5), 482–488. https://doi.org/10.1037/h0045106
  8. Tulving, E., & Pearlstone, Z. (1966). Availability versus accessibility of information in memory for words. Journal of Verbal Learning and Verbal Behavior, 5(4), 381–391. https://doi.org/10.1016/S0022-5371(66)80048-8
  9. Singh, S. N., Rothschild, M. L., & Churchill, G. A. (1988). Recognition versus recall as measures of television commercial forgetting. Journal of Marketing Research, 25(1), 72–80. https://doi.org/10.2307/3172926
  10. Wells, W. D. (2000). Recognition, recall, and rating scales. Journal of Advertising Research, 40(6), 14–19. https://doi.org/10.2501/JAR-40-6-14-20

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