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 Sunday, September 22 2019 5:58am Hongkong Time

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The word guanxi (collocation) and meanings of bribe: Deeply rooted, disgusting, sad endings

Raymond Cheng

What makes bribe so deeply-rooted and disgusting? While this may be very difficult, if not impossible, to quantify, it may be something we can do rather conveniently from the linguistical point of view using some help from statistics.

Using Hunston's words, "the frequent co-occurrence of a lexical item with items expressing a particular evaluative meaning" [1a] [1b] gives the sementic preference or the attitudinal preference of a word, i.e. the true meaning of a word, or alternatively, the guanxi of word, or word guanxi (relationship), can be found by understanding the words that appear around it. So, by looking at the words that co-occur (relate, or having a guanxi) with the word "bribe", we can probably understand more about its true meaning.

Disgust Expression (c) Alexandra Dubovski
Photo © Alexandra Dubovski

To determine the most collocated (co-occuring) words for "bribe", let us turn our attention to the Corpus of Historical American English (COHA) [2], an English text database maintained by Brigham Young University, Provo, UT (USA). The database (or corpus) contains an archive of American text selected from books, magazines, novels, etc., published within the last 200 years (1800-2009).

By searching through COHA, a total of 226 collocates of "bribe" with a "Mutual information" (MI) value of no less than 3.0 [3] or a raw occurrence frequency of at least 1,000 (that is, total occurence count multiplied by occurence percentage with core word) were retrieved from COHA.

These words are then tabulated and grouped into the following tables. Each row lists one co-occuring word (or technically known as a collocate), its total occurence count in COHA, the occurence percentage found with the core word, and its corresponding MI values. A further examination of the 226 collocates reviews that approximately 90% of them fall into several rather distinct groups or meaningful categories.

For simplicity reasons, I shall mainly focus on those with an MI value greater than or at least very near 5.0 (those highlighted in red).

*** Nature in General ***
CONSPIRACY    5230 0.78 M5.94
CRIMES        7230 0.28 M4.44
GUILTY       17779 0.29 M4.52
ILLEGAL       6037 0.27 M4.37
MONEY       146465 0.03 M3.48
PLOT          8532 0.15 M3.58
THEFT         2618 0.31 M4.58
UNLAWFUL      1473 0.27 M4.41
VIOLATIONS    2218 0.14 M3.40

*** Ease and Directness ***
ACCESSIBLE    2932 0.17 M3.74
INDIRECT      2915 0.17 M3.75

*** Possible Style ***
MONTHLY       5432 0.11 M3.11
SYSTEMATIC    3222 0.25 M4.28

*** Size and/or Scale ***
GRANNY        2411 0.12 M3.28
PALTRY        1172 0.34 M4.74
PETTY         5335 0.17 M3.72
LAVISH        2370 0.21 M4.05
COLOSSAL      2113 0.14 M3.47

*** Involvement ***
INVOLVING     6283 0.29 M4.49
PARTICIPATING 1809 0.17 M3.70
RELATING      4739 0.11 M3.05
*** How it started ***
PERVERTED     1284 0.23 M4.19
RESORT        8076 0.11 M3.12
RESORTED      2401 0.25 M4.29
RESORTING      789 0.38 M4.90
PECUNIARY     2244 0.13 M3.39

*** How it proceeds ***
BEGGED        7298 0.11 M3.10
COAXED        1018 0.79 M5.94
COAXING        781 0.26 M4.33
INDUCE        4826 0.25 M4.28
INDUCEMENT    1167 0.34 M4.75
INDUCING       814 0.37 M4.85
INTRIGUE      1689 0.77 M5.91
INTRIGUES     1053 0.76 M5.89
LURED         1508 0.20 M3.96
NEGOTIATION   2509 0.16 M3.64
OVERLOOK      2390 0.17 M3.71
PERSUADE      6358 0.11 M3.11
PERSUASION    2500 0.40 M4.97
POSE          3480 0.14 M3.49
PROCURED      3000 0.17 M3.71
PROFFERED     1351 0.37 M4.86
PROMISES      8720 0.22 M4.09
TEMPT         1998 0.30 M4.56
TEMPTING      2296 0.26 M4.35
OUTRIGHT      2905 0.59 M5.52
UNSCRUPULOUS  1592 0.25 M4.30

Table 1: General nature of "bribe"

General meaning of bribe – my personal interpretation

Table 1 contains collocates of bribe that describe its meaning in general. Unlike armed robbery or any other violent crimes, bribe, as described by its collocates, can be (in my opinion) thought of as a slowly proceeding, gently persuaded, coaxed (MI=5.94) and intrigued (MI=5.89, MI=5.91) crime that normally disguises itself as an open, direct and outright (MI=5.52) action involving paltry (MI=4.74) amount of money. It is, in fact, one that is well-planned, a conspiracy (MI=5.94), and involves lavish (MI=4.05) amount of money, possibly both in a systematic (MI=4.28) and even monthly (MI=3.11) manner.

However, does that mean petty corruption is something we do not have to worry? I'm afraid not. Bearing in mind that our statistics come from COHA, Corpus of Historical American English, the collocates here reflect only the American scenario, and obviously not for Nigeria, Pakistan or any other countries.

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Note 1a: For explanations on the "lexical item", please see my other article on "John Sinclair's lexical item".

Note 1b: See p. 265-266 in Hunston, S. (2007). Semantic prosody revisited. International Journal of Corpus Linguistics, 12(2), 249-268.

Note 2: Corpus of Historical American English at Brigham Young University, see

Note 3: A Mutual Information (MI) value of 2.0 or 3.0 means high frequency "noise words" like the, of, is, am, are, etc. will be ignored. Typically, MI values of 3.0 or above shows a "semantic bonding" between the words and are therefore more meaningful in terms of collocation analysis. Too small MI values indicate words simply collocates, for example, grammatical words like is, am, are, etc. and are therefore ignored in this research. Meanwhile, a major limitation of this research requires that only content words will be taken as a core whereas other meaningful words, like the phrasal verbs, are not considered.

Commentary and reflection pages by Raymond Cheng, PhD DPA FRSA

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COUNT ON THE STATISTICS  100% Towels (c) Daniel Chittka
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This new section contains some interesting statistics in bribe and corruption, please check back for more as we pile up our numbers!

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  The word guanxi (collocation) and meanings of bribe: Deeply rooted, disgusting, sad endings

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Oh, please do not get me wrong. This new section is not about computers, electronics or any engineering stuff, but rather I am currently constructing a new corpus based on Spectrum, the monthly publication from the Institute of Electrical and Electronics Engineers USA, from July 2007 to date. Having been a member for over 20 years since 1992, I am always fascinated by some of the terms scientists use when they talk about or envision their new inventions or methodologies. How many of them eventually come into practice? Could there be some insights we could possibly derive, from the linguistics perspective?


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