What is she best known for?
The fields of study she is best known for:
- Linguistics
- Mathematics education
- Algebra
The scientist’s investigation covers issues in Linguistics, Hebrew, Noun, Language acquisition and Psycholinguistics.
Her research on Linguistics frequently links to adjacent areas such as Literacy.
Her Hebrew research includes themes of Spelling, Orthography, Inflection and Semitic languages.
Her Language acquisition research integrates issues from Second-language attrition, Cognitive psychology, Language transfer, First language and Second-language acquisition.
Her work deals with themes such as Linguistic description, Linguistic purism, Language industry, Linguistic competence and Deep linguistic processing, which intersect with Written language.
The various areas that Dorit Ravid examines in her Morpheme study include Mental lexicon, Word order, Root and Reading.
Her most cited work include:
- Use of enalapril to attenuate decline in renal function in normotensive, normoalbuminuric patients with type 2 diabetes mellitus. A randomized, controlled trial. (336 citations)
- Developing linguistic literacy: a comprehensive model. (266 citations)
- Cross-Linguistic Evidence for the Nature of Age Effects in Second Language Acquisition. (164 citations)
What are the main themes of her work throughout her whole career to date?
Dorit Ravid mainly focuses on Linguistics, Hebrew, Noun, Spelling and Language acquisition.
Her study involves Phonology, Orthography, Lexicon, Root and Semitic languages, a branch of Linguistics.
As a member of one scientific family, Dorit Ravid mostly works in the field of Hebrew, focusing on Reading and, on occasion, Vowel and Context.
Her studies in Noun integrate themes in fields like Agreement, Suffix and Plural.
As part of the same scientific family, Dorit Ravid usually focuses on Spelling, concentrating on Written language and intersecting with Spoken language.
Her Language acquisition research is multidisciplinary, incorporating perspectives in Developmental linguistics, Syntax, Language development and Grammar.
She most often published in these fields:
- Linguistics (76.03%)
- Hebrew (58.22%)
- Noun (23.29%)
What were the highlights of her more recent work (between 2015-2021)?
- Linguistics (76.03%)
- Hebrew (58.22%)
- Verb (7.53%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Linguistics, Hebrew, Verb, Language acquisition and Lexicon.
Her study in Root, Noun, Suffix, Word order and Spelling falls within the category of Linguistics.
Her Hebrew study integrates concerns from other disciplines, such as Developmental psychology, Narrative, Lexical item and Semitic languages.
Her studies examine the connections between Verb and genetics, as well as such issues in Future tense, with regards to Order of acquisition and Past tense.
Her Language acquisition study incorporates themes from Syntax, Morpheme, Language impairment and Conceptualization.
Her Lexicon study combines topics in areas such as Variation, Semantics, Computational linguistics and Grammar.
Between 2015 and 2021, her most popular works were:
- Morpho-lexical development in language impaired and typically developing Hebrew-speaking children from two SES backgrounds (12 citations)
- Breaking into the Hebrew verb system: A learning problem (12 citations)
- Context effects on heterophonic-homography resolution in learning to read hebrew (11 citations)
In her most recent research, the most cited papers focused on:
- Linguistics
- Mathematics education
- Algebra
Her primary areas of investigation include Hebrew, Linguistics, Root, Language acquisition and Developmental psychology.
Her Hebrew research includes elements of Adjective and Natural language processing.
Many of her studies on Linguistics involve topics that are commonly interrelated, such as Artificial intelligence.
Her Language acquisition study combines topics in areas such as Morpheme and Verb.
Her work deals with themes such as Context and Reading, which intersect with Developmental psychology.
Her Reading research is multidisciplinary, relying on both Matched control and Knowledge level.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
