Lexigenesis in ancestral south-east Australian Aboriginal language

Christopher Illert

Journal of Applied Statistics, 2003, vol. 30, issue 2, 113-143

Abstract: The 1/x frequency distribution is known to researchers ranging from economists and biologists to electronic engineers. It is known to linguists as Zipf's Law (Zipf, 1949) and has recently been shown not to be a consequence of the Central Limit Theorem (Troll & Graben, 1998)--leaving an "unsolved problem' in information theory (Jones, 1999). This 1/x distribution, associated with scale-invariant physical systems (Machlup & Hoshiko, 1980), is a special case of the general power law xλ arising from the Lagrangian L(x,F-super-˙(x)) = ½x1-λF-super-˙2 and, as λ need not be an integer, some related research understandably involves fractals (Allison et al. , 2001). The present paper generalizes this Lagrangian to include a van der Waals effect. It is argued that ancestral Aboriginal language consisted of root-morphemes that were built up into, and often condensed within, subsequent words or lexemes. Using discrete-optimization techniques pioneered elsewhere (Illert, 1987; Reverberi, 1985), and the new morpho-statistics, this paper models lexeme-condensation in ancestral south-east Australian Aboriginal language.

Date: 2003
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/0266476022000023703 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:30:y:2003:i:2:p:113-143

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/0266476022000023703

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().