RD-1029.8.16.1-V (2020-09)
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2.2 Qualified expenditures
Break down the R&D expenditures by entering them in columns A, B and C. In column A, enter the R&D expenditures made in the taxation year by the taxpayer.
In column B, enter the portion of the consideration attributable to R&D work that the taxpayer paid in the taxation year to a subcontractor not dealing at
arm’s length with the taxpayer. In column C, enter the portion of the consideration attributable to R&D work that the taxpayer paid in the taxation year to a
subcontractor dealing at arm’s length with the taxpayer.
A B C
Taxpayer Non-arm’s-length Arm’s-length
subcontractor subcontractor
4
Qualified expenditure, if the proxy method was not chosen on form RD-222-V 125
Qualified expenditure, if the proxy method was chosen on form RD-222-V 126
5
Proxy amount, if the proxy method was chosen on form RD-222-V + 127 N/A N/A
Add lines 126 and 127. = 128
Amount from line 125 or line 128, as the case may be 130
Contributions received for the R&D project 131
6 7 8
Assistance, benefit or advantage, or contract payment related to the amount
on line 130 + 148
Add lines 131 and 148. = 149
Subtract line 149 from line 130. Qualified expenditures 150
2.3 Information about the subcontractors dealing at arm’s length with the taxpayer
If you entered an amount in column C, provide the following information about the subcontractors that were dealing at arm’s length with the taxpayer.
If there is not enough space, enclose an additional copy of the form.
Name of subcontractor Identification number (QST)
163.1 164.1
Address Postal code Social insurance number
165.1 166.1 167.1
Total amount of consideration Portion paid during the year
168.1 169.1
Name of subcontractor Identification number (QST)
163.2 164.2
Address Postal code Social insurance number
165.2 166.2 167.2
Total amount of consideration Portion paid during the year
168.2 169.2
13D0 ZZ 49516848

Armenian (not currently supported for detection) is being labeled as English with a confidence of 1.

Examples:

For input text: 'Տուն'
The detected language is 'en'. Confidence is: 1.

For input text: 'Անձնական Տվյալների Ցուցակ'
The detected language is 'en'. Confidence is: 1.

For input text: 'Ժողովրդագրություն'
The detected language is 'en'. Confidence is: 1.

For input text: 'Օժանդակ'
The detected language is 'en'. Confidence is: 1.

For input text: 'Կապի անհատներ'
The detected language is 'en'. Confidence is: 1.

For input text: 'Տվյալների Հաստատում'
The detected language is 'en'. Confidence is: 1.

Many many more examples. Meanwhile, other popular online language detectors detect these correctly. :(

Note: We are aware Armenian is not supported for detection (currently has over 30 votes to be added). This is a separate issue of incorrect confidence scores in detecting English. The expected result for an unsupported language was expected to be a low confidence score, especially when the text checked isn't even using English based characters. Please take action.

The lang detection API provides a score. The only details are that 1.0 indicates the highest level of confidence, and that a lower score will be returned in mixed language situations. Here are a couple of cases which don't seem to agree:

The example in the docs, leaving the english and spanish, but removing the french, is still mixed:

Hello, I would like to take a class at your University. ¿Se ofrecen clases en español? Es mi primera lengua y más fácil para escribir.

--> returns Spanish, score 1

Hey, that's life. C'est la vie!

--> returns French, score 1

should almost certainly return English, as "C'est la vie" is an "imported phrase" often used in English. It's presence in text along with the rest of the text being English almost certainly means the result should be English.

Generally, the score seems to always be 1 (or very close to 1 as in the doc example, which probably should be a much lower value than 0.9375). The score could be more useful if it used the whole 0..1 range.